Latest open access articles published in Healthcraft Frontiers at https://www.acadlore.com/journals/HF https://www.acadlore.com/journals/HF Acadlore en Creative Commons Attribution(CC - BY) HF support@acadlore.com https://www.acadlore.com/article/HF/2025_3_3/hf030302 Healthcare facilities generate heterogeneous waste streams that must be accurately segregated at the point of disposal to mitigate occupational exposure risks, reduce downstream treatment costs, and ensure compliance with stringent biomedical waste regulations. However, most existing automated waste segregation systems have been developed for domestic or general-purpose scenarios and are poorly adapted to the operational complexity and safety requirements of hospital environments. In this study, a hospital-specific automated waste segregation system was designed, implemented, and experimentally evaluated for real-time classification of five clinically relevant waste categories: infectious waste, sharps, pharmaceutical waste, recyclable waste, and general waste. The proposed system integrates an ultrasonic sensor with a Raspberry Pi 4B platform executing a lightweight MobileNetV2 model, coupled with a motorised mechanical sorting mechanism. A curated dataset comprising 6,868 labelled hospital-waste images was constructed and used to fine-tune the model to ensure robustness under embedded deployment constraints. Experimental validation under simulated hospital disposal scenarios demonstrated an overall classification accuracy of 97%, with end-to-end segregation cycle times ranging from 8 to 12 seconds per item across repeated trials. These results indicate that high-accuracy, real-time waste classification can be achieved using low-cost embedded hardware and compact deep learning architectures. The proposed approach establishes a practical and scalable foundation for intelligent healthcare waste management at the point of disposal, offering a viable pathway toward safer clinical environments, improved operational efficiency, and the broader adoption of edge AI solutions in resource-constrained healthcare settings. 09-29-2025 <![CDATA[ Healthcare facilities generate heterogeneous waste streams that must be accurately segregated at the point of disposal to mitigate occupational exposure risks, reduce downstream treatment costs, and ensure compliance with stringent biomedical waste regulations. However, most existing automated waste segregation systems have been developed for domestic or general-purpose scenarios and are poorly adapted to the operational complexity and safety requirements of hospital environments. In this study, a hospital-specific automated waste segregation system was designed, implemented, and experimentally evaluated for real-time classification of five clinically relevant waste categories: infectious waste, sharps, pharmaceutical waste, recyclable waste, and general waste. The proposed system integrates an ultrasonic sensor with a Raspberry Pi 4B platform executing a lightweight MobileNetV2 model, coupled with a motorised mechanical sorting mechanism. A curated dataset comprising 6,868 labelled hospital-waste images was constructed and used to fine-tune the model to ensure robustness under embedded deployment constraints. Experimental validation under simulated hospital disposal scenarios demonstrated an overall classification accuracy of 97%, with end-to-end segregation cycle times ranging from 8 to 12 seconds per item across repeated trials. These results indicate that high-accuracy, real-time waste classification can be achieved using low-cost embedded hardware and compact deep learning architectures. The proposed approach establishes a practical and scalable foundation for intelligent healthcare waste management at the point of disposal, offering a viable pathway toward safer clinical environments, improved operational efficiency, and the broader adoption of edge AI solutions in resource-constrained healthcare settings. ]]> Hospital-Specific Automated Waste Segregation for High-Accuracy Real-Time Classification sultan akinola atanda shuqroh opeyemi abdulrasaq olusola kunle akinde sunday adeola ajagbe abraham kehinde aworinde doi: 10.56578/hf030302 Healthcraft Frontiers 09-29-2025 Healthcraft Frontiers 09-29-2025 2025 3 3 Article 128 10.56578/hf030302 https://www.acadlore.com/article/HF/2025_3_3/hf030302 https://www.acadlore.com/article/HF/2025_3_3/hf030301 Metformin remains the cornerstone of pharmacological management for Type 2 diabetes mellitus (T2DM); however, its long-term use has been associated with impaired vitamin B12 absorption and macrocytosis. The prevalence and independent determinants of macrocytosis in metformin-treated populations remain insufficiently characterized. A cross-sectional observational study was conducted at Saidu Teaching Hospital, Swat, between July 2024 and August 2025. A total of 236 adults with T2DM receiving metformin therapy for at least six months were enrolled. Macrocytosis was defined as a mean corpuscular volume (MCV) exceeding 100 fL. Demographic characteristics, clinical parameters, and concomitant medication use were systematically recorded. Univariate analyses were initially performed, followed by multivariate logistic regression to identify independent predictors of macrocytosis. The overall prevalence of macrocytosis was 16.1% (95% confidence interval (CI): 11.7–21.4%). Multivariate analysis demonstrated that metformin therapy duration >4 years (odds ratio (OR): 3.42, p = 0.002), daily metformin dose ≥1500 mg (OR: 2.89, p = 0.006), concomitant proton pump inhibitor (PPI) use (OR: 4.15, p p = 0.030) were independently associated with macrocytosis, with concurrent PPI use emerging as the strongest predictor. These findings indicate that macrocytosis is a relatively common hematologic abnormality in metformin-treated adults with T2DM and is strongly influenced by treatment duration, dosage intensity, age, and concurrent PPI therapy. Risk-stratified surveillance strategies incorporating periodic assessment of MCV and serum vitamin B12 levels may therefore be warranted, particularly in high-risk patients, to enhance patient safety and optimize the long-term clinical management of T2DM. 09-29-2025 <![CDATA[ <p>Metformin remains the cornerstone of pharmacological management for Type 2 diabetes mellitus (T2DM); however, its long-term use has been associated with impaired vitamin B12 absorption and macrocytosis. The prevalence and independent determinants of macrocytosis in metformin-treated populations remain insufficiently characterized. A cross-sectional observational study was conducted at Saidu Teaching Hospital, Swat, between July 2024 and August 2025. A total of 236 adults with T2DM receiving metformin therapy for at least six months were enrolled. Macrocytosis was defined as a mean corpuscular volume (MCV) exceeding 100 fL. Demographic characteristics, clinical parameters, and concomitant medication use were systematically recorded. Univariate analyses were initially performed, followed by multivariate logistic regression to identify independent predictors of macrocytosis. The overall prevalence of macrocytosis was 16.1% (95% confidence interval (CI): 11.7–21.4%). Multivariate analysis demonstrated that metformin therapy duration >4 years (odds ratio (OR): 3.42, <em>p</em> = 0.002), daily metformin dose ≥1500 mg (OR: 2.89, <em>p</em> = 0.006), concomitant proton pump inhibitor (PPI) use (OR: 4.15, <em>p</em> < 0.001), and age ≥60 years (OR: 2.26, <em>p</em> = 0.030) were independently associated with macrocytosis, with concurrent PPI use emerging as the strongest predictor. These findings indicate that macrocytosis is a relatively common hematologic abnormality in metformin-treated adults with T2DM and is strongly influenced by treatment duration, dosage intensity, age, and concurrent PPI therapy. Risk-stratified surveillance strategies incorporating periodic assessment of MCV and serum vitamin B12 levels may therefore be warranted, particularly in high-risk patients, to enhance patient safety and optimize the long-term clinical management of T2DM.</p> ]]> Prevalence and Independent Predictors of Macrocytosis among Metformin-Treated Patients with Type 2 Diabetes Mellitus hamad ali ebad ali qaisar ali nadir ahmad hammad khan idrees khan doi: 10.56578/hf030301 Healthcraft Frontiers 09-29-2025 Healthcraft Frontiers 09-29-2025 2025 3 3 Article 121 10.56578/hf030301 https://www.acadlore.com/article/HF/2025_3_3/hf030301 https://www.acadlore.com/article/HF/2025_3_2/hf030205 Microplastics, commonly defined as plastic particles smaller than 5 mm, have emerged as pervasive contaminants across natural and anthropogenic systems, constituting a complex global stressor with environmental, economic, and public health implications. Since the term microplastic was first introduced in 2004, an expanding body of research has revealed the extensive diversity and abundance of these particles, which originate from both the fragmentation of larger plastic debris (secondary microplastics) and the intentional production of microscopic polymers (primary microplastics) for use in cosmetics, industrial abrasives, and synthetic textiles. Despite substantial scientific attention, the absence of a universally accepted classification framework–particularly with respect to size ranges, polymer composition, and source attribution–continues to hinder harmonized monitoring and regulatory action. Microplastics have been detected in marine and freshwater environments, terrestrial soils, atmospheric fallout, and remote regions, demonstrating their capacity for long-range transport through hydrological, atmospheric, and biogeochemical processes. Ecologically, exposure has been shown to impair feeding behavior, induce physical obstruction, and compromise reproductive success across multiple trophic levels, from planktonic organisms to higher vertebrates. Chemically, microplastics function as dynamic carriers for persistent organic pollutants, heavy metals, and microbial assemblages. Human exposure has been increasingly documented through dietary intake, drinking water consumption, and inhalation of airborne particles. Effective mitigation of microplastic pollution will require coordinated international policy frameworks, advances in materials innovation and waste management, standardized analytical methodologies, and sustained public engagement to address both sources and impacts of microplastic contamination. 06-09-2025 <![CDATA[ <p><span style="color: black; font-family: Times New Roman, serif">Microplastics, commonly defined as plastic particles smaller than 5 mm, have emerged as pervasive contaminants across natural and anthropogenic systems, constituting a complex global stressor with environmental, economic, and public health implications. Since the term microplastic was first introduced in 2004, an expanding body of research has revealed the extensive diversity and abundance of these particles, which originate from both the fragmentation of larger plastic debris (secondary microplastics) and the intentional production of microscopic polymers (primary microplastics) for use in cosmetics, industrial abrasives, and synthetic textiles. Despite substantial scientific attention, the absence of a universally accepted classification framework–particularly with respect to size ranges, polymer composition, and source attribution–continues to hinder harmonized monitoring and regulatory action. Microplastics have been detected in marine and freshwater environments, terrestrial soils, atmospheric fallout, and remote regions, demonstrating their capacity for long-range transport through hydrological, atmospheric, and biogeochemical processes. Ecologically, exposure has been shown to impair feeding behavior, induce physical obstruction, and compromise reproductive success across multiple trophic levels, from planktonic organisms to higher vertebrates. Chemically, microplastics function as dynamic carriers for persistent organic pollutants, heavy metals, and microbial assemblages. Human exposure has been increasingly documented through dietary intake, drinking water consumption, and inhalation of airborne particles. Effective mitigation of microplastic pollution will require coordinated international policy frameworks, advances in materials innovation and waste management, standardized analytical methodologies, and sustained public engagement to address both sources and impacts of microplastic contamination.</span></p> ]]> Microplastics: A Multidimensional Threat to Environment, Economy, and Public Health anushka basu rohith kumar pagadala doi: 10.56578/hf030205 Healthcraft Frontiers 06-09-2025 Healthcraft Frontiers 06-09-2025 2025 3 2 Article 110 10.56578/hf030205 https://www.acadlore.com/article/HF/2025_3_2/hf030205 https://www.acadlore.com/article/HF/2025_3_2/hf030204 Drug-therapy problems (DTPs) are a major concern in cardiovascular disease (CVD) management, particularly among older adults exposed to polypharmacy, drug-drug interactions (DDIs), non-adherence, unnecessary drug therapy, and adverse drug reactions (ADRs). This prospective observational study, conducted in the cardiology unit of Saidu Group of Teaching Hospital (SGTH), Swat, Pakistan, evaluated 350 inpatients admitted from January to March 2022. Data were analyzed using SPSS v26 and GraphPad Prism v5.01, employing chi-square tests, t-tests, and binary logistic regression (p 5 medications). DTP frequency correlated positively with age and the number of prescribed medications, particularly among individuals aged 31–70 years. The findings indicate a high burden of preventable DTPs in CVD management, dominated by DDIs and unnecessary drug therapy. The incorporation of multidisciplinary medication-review teams, strengthened clinical decision support systems (CDSS), and routine prescription audits is recommended to mitigate DTPs and enhance the safety and precision of cardiovascular pharmacotherapy. 05-26-2025 <![CDATA[ <p>Drug-therapy problems (DTPs) are a major concern in cardiovascular disease (CVD) management, particularly among older adults exposed to polypharmacy, drug-drug interactions (DDIs), non-adherence, unnecessary drug therapy, and adverse drug reactions (ADRs). This prospective observational study, conducted in the cardiology unit of Saidu Group of Teaching Hospital (SGTH), Swat, Pakistan, evaluated 350 inpatients admitted from January to March 2022. Data were analyzed using SPSS v26 and GraphPad Prism v5.01, employing chi-square tests, t-tests, and binary logistic regression (<em>p</em> < 0.05). A total of 323 patients (92.29%) experienced at least one DTP, accounting for 1,252 events. DDIs were the most common DTP, followed by unnecessary drug therapy and ADRs. Age was a significant predictor, with the highest odds of DTPs observed in patients aged 61–70 years; a slight, non-significant increase was noted among females and those with polypharmacy (>5 medications). DTP frequency correlated positively with age and the number of prescribed medications, particularly among individuals aged 31–70 years. The findings indicate a high burden of preventable DTPs in CVD management, dominated by DDIs and unnecessary drug therapy. The incorporation of multidisciplinary medication-review teams, strengthened clinical decision support systems (CDSS), and routine prescription audits is recommended to mitigate DTPs and enhance the safety and precision of cardiovascular pharmacotherapy.</p> ]]> Evaluation of Drug-Therapy Problems in Cardiovascular Disease Management: Implications for Future Clinical Practice muhammad esa roqia bibi muhammad abbas amanat doi: 10.56578/hf030204 Healthcraft Frontiers 05-26-2025 Healthcraft Frontiers 05-26-2025 2025 3 2 Article 97 10.56578/hf030204 https://www.acadlore.com/article/HF/2025_3_2/hf030204 https://www.acadlore.com/article/HF/2025_3_2/hf030203 Deep learning (DL) has increasingly been adopted to support automated medical diagnosis, particularly in radiological imaging where rapid and reliable interpretation is essential. In this study, a hybrid architecture integrating convolutional neural network (CNN), residual networks (ResNet), and densely connected networks (DenseNet) was developed to improve automated disease recognition in chest X-ray images. This unified framework was designed to capture shallow, residual, and densely connected representations simultaneously, thereby strengthening feature diversity and improving classification robustness relative to conventional single-model or dual-model approaches. The model was trained and evaluated using the ChestX-ray14 dataset, comprising more than 100,000 X-ray images representing 14 thoracic disease classes. Performance was assessed using established metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). A classification accuracy of 92.5% was achieved, representing an improvement over widely used machine learning (ML) and contemporary DL baselines. To promote transparency and clinical interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated, enhancing clinician confidence in model decisions. The findings demonstrate that DL-based diagnostic support systems can reduce diagnostic uncertainty, alleviate clinical workload, and facilitate timely decision-making in healthcare environments. The proposed hybrid model illustrates the potential of advanced feature-integration strategies to improve automated radiographic interpretation and underscores the importance of explainable artificial intelligence (XAI) in promoting trustworthy deployment of medical artificial intelligence (AI) technologies. 05-08-2025 <![CDATA[ Deep learning (DL) has increasingly been adopted to support automated medical diagnosis, particularly in radiological imaging where rapid and reliable interpretation is essential. In this study, a hybrid architecture integrating convolutional neural network (CNN), residual networks (ResNet), and densely connected networks (DenseNet) was developed to improve automated disease recognition in chest X-ray images. This unified framework was designed to capture shallow, residual, and densely connected representations simultaneously, thereby strengthening feature diversity and improving classification robustness relative to conventional single-model or dual-model approaches. The model was trained and evaluated using the ChestX-ray14 dataset, comprising more than 100,000 X-ray images representing 14 thoracic disease classes. Performance was assessed using established metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). A classification accuracy of 92.5% was achieved, representing an improvement over widely used machine learning (ML) and contemporary DL baselines. To promote transparency and clinical interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated, enhancing clinician confidence in model decisions. The findings demonstrate that DL-based diagnostic support systems can reduce diagnostic uncertainty, alleviate clinical workload, and facilitate timely decision-making in healthcare environments. The proposed hybrid model illustrates the potential of advanced feature-integration strategies to improve automated radiographic interpretation and underscores the importance of explainable artificial intelligence (XAI) in promoting trustworthy deployment of medical artificial intelligence (AI) technologies. ]]> Hybrid Deep Learning Architecture for Automated Chest X-ray Disease Detection with Explainable Artificial Intelligence b. vivekanandam kambala vijaya kumar jagadeeswara rao annam doi: 10.56578/hf030203 Healthcraft Frontiers 05-08-2025 Healthcraft Frontiers 05-08-2025 2025 3 2 Article 86 10.56578/hf030203 https://www.acadlore.com/article/HF/2025_3_2/hf030203 https://www.acadlore.com/article/HF/2025_3_2/hf030202 Diabetic foot infections (DFIs) are a major cause of morbidity and lower-limb amputations among individuals with diabetes mellitus. Inappropriate empirical antibiotic use contributes to treatment failure and elevated amputation risk. This observational study, conducted across six hospitals in Khyber Pakhtunkhwa (KP), Pakistan, involved 341 patients with clinically diagnosed DFIs. The objectives were to evaluate antibiotic efficacy, treatment outcomes, and risk factors for amputation, and to develop a visual risk stratification model correlating antibiotic response with amputation risk. The cohort exhibited a significant male predominance (64.5%, p = 0.003), with the highest prevalence among patients aged 41–50 years (36.7%). Most participants were insulin-independent (92.7%, p 04-23-2025 <![CDATA[ <p>Diabetic foot infections (DFIs) are a major cause of morbidity and lower-limb amputations among individuals with diabetes mellitus. Inappropriate empirical antibiotic use contributes to treatment failure and elevated amputation risk. This observational study, conducted across six hospitals in Khyber Pakhtunkhwa (KP), Pakistan, involved 341 patients with clinically diagnosed DFIs. The objectives were to evaluate antibiotic efficacy, treatment outcomes, and risk factors for amputation, and to develop a visual risk stratification model correlating antibiotic response with amputation risk. The cohort exhibited a significant male predominance (64.5%, <em>p</em> = 0.003), with the highest prevalence among patients aged 41–50 years (36.7%). Most participants were insulin-independent (92.7%, <em>p</em> < 0.0001). Infection severity was mild in 28.7%, moderate in 47.8%, and severe in 23.5% of cases. Clinical outcomes included complete recovery (39.3%), improvement (31.7%), progression (19.9%), and amputation (9.1%). High-efficacy antibiotics included Levofloxacin (Levaquin, 100%), Colistin (100%), and Linezolid (Zyvox, 86.6%), whereas Ceftriaxone (Cefzone, 33.3%), Ampicillin/Sulbactam (Penro, 38.3%), and Clindamycin (Cleocin HCl, 26.6%) demonstrated limited therapeutic benefit. The visual stratification model showed that exposure to low-efficacy antibiotics significantly increased amputation risk. Logistic regression identified severe baseline infection (odds ratio (OR) ≈3.2), poor glycemic control (OR ≈ 1.9), and treatment with low-efficacy antibiotics (OR ≈ 2.8) as independent predictors of unfavorable outcomes. This study highlights the need for region-specific antibiotic stewardship, continuous resistance surveillance, and evidence-based treatment protocols. The proposed visual model offers a practical framework for guiding empirical therapy and reducing amputation rates in DFI management.</p> ]]> Visual Risk Stratification of Antibiotic Efficacy as a Predictor of Amputation Risk in Diabetic Foot Infection Management azmat ullah jan ihsan ullah doi: 10.56578/hf030202 Healthcraft Frontiers 04-23-2025 Healthcraft Frontiers 04-23-2025 2025 3 2 Article 76 10.56578/hf030202 https://www.acadlore.com/article/HF/2025_3_2/hf030202 https://www.acadlore.com/article/HF/2025_3_2/hf030201 The pervasive integration of plastic materials into contemporary society has yielded substantial societal and economic advantages, yet has concurrently precipitated growing toxicological concerns with significant implications for human health. This study critically examines the multifaceted health impacts associated with chronic exposure to microplastics and plastic-derived chemical additives, including phthalates, bisphenol A (BPA), flame retardants, and heavy metals. Through a comprehensive synthesis of recent toxicological and epidemiological evidence, the mechanisms through which these contaminants disrupt endocrine regulation, impair immune homeostasis, and compromise cellular function are elucidated. Cumulative exposure has been linked to heightened incidences of hormone-related disorders, carcinogenesis, metabolic syndromes, and neurodevelopmental abnormalities. Recent advances in analytical detection techniques have confirmed the systemic distribution and bioaccumulation of microplastic particles across human organs. Environmental vectors—such as air, water, soil, and food contamination—serve as major conduits of microplastic exposure, amplifying indirect toxicological risks through trophic transfer and persistent environmental deposition. Despite the mounting evidence of harm, current regulatory frameworks remain fragmented and insufficiently stringent, reflecting a lag between scientific understanding and policy enforcement. Addressing these deficiencies requires a paradigm shift from reactive risk management toward proactive prevention, encompassing the development of biodegradable materials, reinforcement of global monitoring systems, and the establishment of harmonized exposure thresholds. The synthesis presented herein highlights the urgent necessity of redefining plastic consumption and waste management practices to safeguard both human and ecological health, advocating for integrative strategies that align environmental sustainability with public health protection. 04-13-2025 <![CDATA[ The pervasive integration of plastic materials into contemporary society has yielded substantial societal and economic advantages, yet has concurrently precipitated growing toxicological concerns with significant implications for human health. This study critically examines the multifaceted health impacts associated with chronic exposure to microplastics and plastic-derived chemical additives, including phthalates, bisphenol A (BPA), flame retardants, and heavy metals. Through a comprehensive synthesis of recent toxicological and epidemiological evidence, the mechanisms through which these contaminants disrupt endocrine regulation, impair immune homeostasis, and compromise cellular function are elucidated. Cumulative exposure has been linked to heightened incidences of hormone-related disorders, carcinogenesis, metabolic syndromes, and neurodevelopmental abnormalities. Recent advances in analytical detection techniques have confirmed the systemic distribution and bioaccumulation of microplastic particles across human organs. Environmental vectors—such as air, water, soil, and food contamination—serve as major conduits of microplastic exposure, amplifying indirect toxicological risks through trophic transfer and persistent environmental deposition. Despite the mounting evidence of harm, current regulatory frameworks remain fragmented and insufficiently stringent, reflecting a lag between scientific understanding and policy enforcement. Addressing these deficiencies requires a paradigm shift from reactive risk management toward proactive prevention, encompassing the development of biodegradable materials, reinforcement of global monitoring systems, and the establishment of harmonized exposure thresholds. The synthesis presented herein highlights the urgent necessity of redefining plastic consumption and waste management practices to safeguard both human and ecological health, advocating for integrative strategies that align environmental sustainability with public health protection. ]]> Toxicological Consequences of Microplastic Exposure on Human Health: Mechanisms, Pathways, and Systemic Outcomes anushka basu rohith kumar pagadala harsh purohit doi: 10.56578/hf030201 Healthcraft Frontiers 04-13-2025 Healthcraft Frontiers 04-13-2025 2025 3 2 Article 58 10.56578/hf030201 https://www.acadlore.com/article/HF/2025_3_2/hf030201 https://www.acadlore.com/article/HF/2025_3_1/hf030105 The transformative potential of artificial intelligence (AI) and machine learning (ML) in healthcare has been increasingly recognized, particularly in medical image analysis and predictive modeling of patient outcomes. In this study, a novel convolutional neural network (CNN) architecture incorporating customized skip connections was introduced to enhance feature extraction and accelerate convergence during medical image classification. This model demonstrated superior performance compared with conventional architectures such as Residual Network with 50 layers (ResNet-50) and Visual Geometry Group with 16 layers (VGG16), achieving an accuracy of 96.5% along with improved precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). In parallel, patient readmission risks were predicted using an optimized random forest algorithm, which, after hyperparameter tuning, attained a robust AUC-ROC value of 0.91, thereby underscoring its stability and predictive reliability. The integration of these approaches highlights the ability of AI and ML systems to deliver more accurate diagnoses, anticipate potential health risks, and recommend personalized treatment strategies, ultimately enabling faster and more precise clinical decision-making. Despite these advancements, challenges persist regarding data privacy, interpretability of AI-driven decisions, and the ethical use of patient information. Addressing these limitations will be critical for the broader adoption of AI-enabled healthcare systems. The findings of this study reinforce the role of advanced AI and ML frameworks in improving healthcare delivery, optimizing the use of limited resources, and reducing operational costs, thereby supporting more effective patient care. 03-27-2025 <![CDATA[ <p>The transformative potential of artificial intelligence (AI) and machine learning (ML) in healthcare has been increasingly recognized, particularly in medical image analysis and predictive modeling of patient outcomes. In this study, a novel convolutional neural network (CNN) architecture incorporating customized skip connections was introduced to enhance feature extraction and accelerate convergence during medical image classification. This model demonstrated superior performance compared with conventional architectures such as Residual Network with 50 layers (ResNet-50) and Visual Geometry Group with 16 layers (VGG16), achieving an accuracy of 96.5% along with improved precision, recall, and area under the receiver operating characteristic curve (AUC-ROC). In parallel, patient readmission risks were predicted using an optimized random forest algorithm, which, after hyperparameter tuning, attained a robust AUC-ROC value of 0.91, thereby underscoring its stability and predictive reliability. The integration of these approaches highlights the ability of AI and ML systems to deliver more accurate diagnoses, anticipate potential health risks, and recommend personalized treatment strategies, ultimately enabling faster and more precise clinical decision-making. Despite these advancements, challenges persist regarding data privacy, interpretability of AI-driven decisions, and the ethical use of patient information. Addressing these limitations will be critical for the broader adoption of AI-enabled healthcare systems. The findings of this study reinforce the role of advanced AI and ML frameworks in improving healthcare delivery, optimizing the use of limited resources, and reducing operational costs, thereby supporting more effective patient care.</p> ]]> Artificial Intelligence and Machine Learning in Smart Healthcare: Advancing Patient Care and Medical Decision-Making anil kumar pallikonda vinay kumar bandarapalli vipparla aruna doi: 10.56578/hf030105 Healthcraft Frontiers 03-27-2025 Healthcraft Frontiers 03-27-2025 2025 3 1 Article 47 10.56578/hf030105 https://www.acadlore.com/article/HF/2025_3_1/hf030105 https://www.acadlore.com/article/HF/2025_3_1/hf030104 Accurate evaluation of tibial mechanical properties is essential for the diagnosis of bone-related disorders, the monitoring of fracture healing, and the optimization of orthopedic rehabilitation outcomes. Conventional diagnostic approaches, including dual-energy X-ray absorptiometry (DXA) and other imaging-based methods, provide valuable information but are limited by radiation exposure, invasiveness, or insufficient sensitivity to early structural changes. To address these limitations, non-invasive vibration-based techniques have been developed as promising alternatives for quantitative, real-time, and radiation-free assessment of tibial biomechanics. Methods such as resonance frequency analysis (RFA), modal analysis, acoustic emission (AE), and laser doppler vibrometry (LDV) have been applied to estimate parameters including bone stiffness, bone mineral density (BMD), and healing dynamics. By introducing controlled vibrational input and recording the tibial response, structural integrity can be characterized, and early indicators of injury, degeneration, or impaired healing can be detected. Recent advances in high-resolution sensors, signal processing algorithms, and wearable technologies have enhanced the sensitivity and applicability of these techniques, while the integration of machine learning has enabled more robust interpretation of complex biomechanical signals. Despite these advances, significant challenges remain, including inter-patient variability, soft tissue damping effects, and the absence of standardized testing protocols. Furthermore, the clinical translation of vibration-based diagnostics requires validation against established imaging modalities and the development of predictive, individualized models of bone health that integrate artificial intelligence (AI) and multimodal data. Current evidence suggests that vibration analysis has the potential to offer a non-invasive and personalized approach to skeletal monitoring. Future research should focus on addressing methodological limitations, improving standardization, and advancing the integration of vibration assessment into routine orthopedic practice and precision medicine frameworks. 03-13-2025 <![CDATA[ <p><span>Accurate evaluation of tibial mechanical properties is essential for the diagnosis of bone-related disorders, the monitoring of fracture healing, and the optimization of orthopedic rehabilitation outcomes. Conventional diagnostic approaches, including dual-energy X-ray absorptiometry (DXA) and other imaging-based methods, provide valuable information but are limited by radiation exposure, invasiveness, or insufficient sensitivity to early structural changes. To address these limitations, non-invasive vibration-based techniques have been developed as promising alternatives for quantitative, real-time, and radiation-free assessment of tibial biomechanics. Methods such as resonance frequency analysis (RFA), modal analysis, acoustic emission (AE), and </span>laser doppler vibrometry (LDV)<span> have been applied to estimate parameters including bone stiffness, bone mineral density (BMD), and healing dynamics. By introducing controlled vibrational input and recording the tibial response, structural integrity can be characterized, and early indicators of injury, degeneration, or impaired healing can be detected. Recent advances in high-resolution sensors, signal processing algorithms, and wearable technologies have enhanced the sensitivity and applicability of these techniques, while the integration of machine learning has enabled more robust interpretation of complex biomechanical signals. Despite these advances, significant challenges remain, including inter-patient variability, soft tissue damping effects, and the absence of standardized testing protocols. Furthermore, the clinical translation of vibration-based diagnostics requires validation against established imaging modalities and the development of predictive, individualized models of bone health that integrate artificial intelligence (AI) and multimodal data. Current evidence suggests that vibration analysis has the potential to offer a non-invasive and personalized approach to skeletal monitoring. Future research should focus on addressing methodological limitations, improving standardization, and advancing the integration of vibration assessment into routine orthopedic practice and precision medicine frameworks.</span></p> ]]> Non-Invasive Vibration-Based Assessment of Tibial Mechanical Properties: Advances, Challenges, and Prospects hydar saadi hassan al-wasti doi: 10.56578/hf030104 Healthcraft Frontiers 03-13-2025 Healthcraft Frontiers 03-13-2025 2025 3 1 Article 27 10.56578/hf030104 https://www.acadlore.com/article/HF/2025_3_1/hf030104 https://www.acadlore.com/article/HF/2025_3_1/hf030103 Diabetic foot ulcers (DFUs), often exacerbated by secondary bacterial infections, are a major complication of diabetes and a leading cause of morbidity. Understanding the spectrum of bacterial pathogens and their profiles of antibiotic resistance is essential for developing effective treatment strategies. This study aimed to identify bacterial isolates from the DFUs and evaluate their susceptibility to commonly used antibiotics. A total of 186 patients with the DFUs were examined at Hayatabad Medical Complex of Peshawar in Pakistan over a three-month period. Samples were collected from infected ulcer sites and cultured with standard microbiological techniques. Bacterial identification was performed with conventional methods, and antibiotic susceptibility testing was then conducted by using the Kirby-Bauer disk diffusion method. Gram-Negative bacteria were predominant, with Pseudomonas aeruginosa, Proteus mirabilis, Acinetobacter spp., Escherichia coli, Klebsiella spp., Enterobacter spp., and Streptococcus pyogenes being the most commonly isolated organisms. Gram-Positive isolates including Staphylococcus aureus and Staphylococcus epidermidis, P. aeruginosa, and Enterobacter spp. showed high sensitivity to Gentamicin, Meropenem, and Imipenem. In contrast, Acinetobacter spp. and Klebsiella spp. exhibited significant resistance, particularly to carbapenems. Staph. aureus was generally sensitive to first-line antibiotics, such as Vancomycin and Rifampicin whereas Staph. epidermidis demonstrated multidrug resistance including pan-drug resistance in some cases. These findings highlighted the complex and resistant microbial profiles of diabetic foot infections, thus emphasizing the importance of the culture-guided antibiotic therapy. The emergence of carbapenem-resistant strains underlined the requisites for continuous surveillance, judicious antibiotic use, and improved infection control strategies to aid the recovery of patients. 03-05-2025 <![CDATA[ <p>Diabetic foot ulcers (DFUs), often exacerbated by secondary bacterial infections, are a major complication of diabetes and a leading cause of morbidity. Understanding the spectrum of bacterial pathogens and their profiles of antibiotic resistance is essential for developing effective treatment strategies. This study aimed to identify bacterial isolates from the DFUs and evaluate their susceptibility to commonly used antibiotics. A total of 186 patients with the DFUs were examined at Hayatabad Medical Complex of Peshawar in Pakistan over a three-month period. Samples were collected from infected ulcer sites and cultured with standard microbiological techniques. Bacterial identification was performed with conventional methods, and antibiotic susceptibility testing was then conducted by using the Kirby-Bauer disk diffusion method. Gram-Negative bacteria were predominant, with <em>Pseudomonas aeruginosa</em>, <em>Proteus mirabilis</em>, <em>Acinetobacter spp.</em>, <em>Escherichia coli</em>, <em>Klebsiella spp.</em>, <em>Enterobacter spp.</em>, and <em>Streptococcus pyogenes</em> being the most commonly isolated organisms. Gram-Positive isolates including <em>Staphylococcus aureus</em> and <em>Staphylococcus epidermidis</em>, <em>P. aeruginosa</em>, and <em>Enterobacter spp.</em> showed high sensitivity to Gentamicin, Meropenem, and Imipenem. In contrast, <em>Acinetobacter spp.</em> and <em>Klebsiella spp.</em> exhibited significant resistance, particularly to carbapenems. <em>Staph. aureus</em> was generally sensitive to first-line antibiotics, such as Vancomycin and Rifampicin whereas <em>Staph. epidermidis</em> demonstrated multidrug resistance including pan-drug resistance in some cases. These findings highlighted the complex and resistant microbial profiles of diabetic foot infections, thus emphasizing the importance of the culture-guided antibiotic therapy. The emergence of carbapenem-resistant strains underlined the requisites for continuous surveillance, judicious antibiotic use, and improved infection control strategies to aid the recovery of patients.</p> ]]> Bacterial Isolates and Antibiotic Sensitivity of Patients with Diabetic Foot Infections at Hayatabad Medical Complex of Peshawar, Pakistan zakir ullah doi: 10.56578/hf030103 Healthcraft Frontiers 03-05-2025 Healthcraft Frontiers 03-05-2025 2025 3 1 Article 17 10.56578/hf030103 https://www.acadlore.com/article/HF/2025_3_1/hf030103 https://www.acadlore.com/article/HF/2025_3_1/hf030102 Diabetes mellitus (DM) is a major non-communicable metabolic disorder characterized by persistent hyperglycemia arising from impaired insulin secretion, insulin resistance, or a combination of both. As the global burden of DM continues to rise, understanding its prevalence and associated risk factors in specific populations is critical for the development of effective prevention and management strategies. A cross-sectional study was conducted among 150 residents (75 males and 75 females) attending a tertiary healthcare facility in Mardan, Pakistan. Sociodemographic characteristics, family history, body mass index (BMI), lifestyle behaviors, dietary patterns, psychological stress, and other potential risk factors were assessed using a structured questionnaire, while venous blood samples were collected to confirm the diagnosis of DM. Overall, the prevalence of DM was found to be 34.67% (n=52), with 29.33% (n=44) previously diagnosed and 5.33% (n=8) newly identified during the investigation. A significant sex-related disparity was observed, with prevalence rates of 26.67% (n=20) in males and 42.67% (n=32) in females. Rural residents exhibited a higher prevalence (42.86%, n=33) compared to urban residents (26.03%, n=19). Several risk factors demonstrated a notable association with DM, including advanced age (>60 years: 8.67%, n=13), obesity (12.67%, n=19), low physical activity (26.67%, n=40), smoking (11.33%, n=17), unhealthy dietary patterns (27.33%, n=41), high psychological stress (17.33%, n=26), hypertension (14%, n=21), and a positive family history (27.33%, n=41). The findings indicate an upward trend in the prevalence of DM in the Mardan region. Immediate implementation of targeted interventions, including public health education, lifestyle modification, dietary counseling, and risk factor management, is essential to mitigate the increasing burden of DM in this population. 02-22-2025 <![CDATA[ <p><span>Diabetes mellitus (DM) is a major non-communicable metabolic disorder characterized by persistent hyperglycemia arising from impaired insulin secretion, insulin resistance, or a combination of both. As the global burden of DM continues to rise, understanding its prevalence and associated risk factors in specific populations is critical for the development of effective prevention and management strategies. A cross-sectional study was conducted among 150 residents (75 males and 75 females) attending a tertiary healthcare facility in Mardan, Pakistan. Sociodemographic characteristics, family history, body mass index (BMI), lifestyle behaviors, dietary patterns, psychological stress, and other potential risk factors were assessed using a structured questionnaire, while venous blood samples were collected to confirm the diagnosis of DM. Overall, the prevalence of DM was found to be 34.67% (n=52), with 29.33% (n=44) previously diagnosed and 5.33% (n=8) newly identified during the investigation. A significant sex-related disparity was observed, with prevalence rates of 26.67% (n=20) in males and 42.67% (n=32) in females. Rural residents exhibited a higher prevalence (42.86%, n=33) compared to urban residents (26.03%, n=19). Several risk factors demonstrated a notable association with DM, including advanced age (>60 years: 8.67%, n=13), obesity (12.67%, n=19), low physical activity (26.67%, n=40), smoking (11.33%, n=17), unhealthy dietary patterns (27.33%, n=41), high psychological stress (17.33%, n=26), hypertension (14%, n=21), and a positive family history (27.33%, n=41). The findings indicate an upward trend in the prevalence of DM in the Mardan region. Immediate implementation of targeted interventions, including public health education, lifestyle modification, dietary counseling, and risk factor management, is essential to mitigate the increasing burden of DM in this population.</span></p> ]]> Prevalence and Risk Factors of Diabetes Mellitus in Younger and Older Patients at a Tertiary Healthcare Facility in Mardan, Pakistan subhan ullah hamad ali sadeeq bacha saeed ullah doi: 10.56578/hf030102 Healthcraft Frontiers 02-22-2025 Healthcraft Frontiers 02-22-2025 2025 3 1 Article 9 10.56578/hf030102 https://www.acadlore.com/article/HF/2025_3_1/hf030102 https://www.acadlore.com/article/HF/2025_3_1/hf030101 Celiac disease (CD) is an autoimmune disorder triggered by gluten in genetically susceptible individuals, and viral infections have been proposed as potential cofactors in its pathogenesis. This study investigated the replication of SEN virus genotypes H and D in CD patients and their association with systemic cytokine levels. A total of 276 participants were enrolled, including 192 CD patients—115 on a gluten-containing diet (GCD) and 77 on a gluten-free diet (GFD)—alongside 84 healthy controls. SEN virus detection was performed using nested PCR, and serum cytokine levels (IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ) were quantified via ELISA. SENV-H was detected in 71.4% of CD patients on a GCD, 66.7% of those on a GFD, and only 19.0% of healthy controls. In contrast, SENV-D was found exclusively in healthy individuals (23.8%) and not in CD patients. CD patients on a GCD exhibited markedly elevated cytokine levels, particularly IL-6 (76.2 ± 12.3 pg/mL), IL-8 (112.0 ± 40.2 pg/mL), and IFN-γ (50.2 ± 15.2 pg/mL), compared to GFD patients and healthy controls. Multivariate logistic regression identified IL-1, IL-2, IL-4, IL-6, IL-8, and IL-10 as significantly associated with active disease (ORs 02-15-2025 <![CDATA[ <p>Celiac disease (CD) is an autoimmune disorder triggered by gluten in genetically susceptible individuals, and viral infections have been proposed as potential cofactors in its pathogenesis. This study investigated the replication of SEN virus genotypes H and D in CD patients and their association with systemic cytokine levels. A total of 276 participants were enrolled, including 192 CD patients—115 on a gluten-containing diet (GCD) and 77 on a gluten-free diet (GFD)—alongside 84 healthy controls. SEN virus detection was performed using nested PCR, and serum cytokine levels (IL-1, IL-2, IL-4, IL-6, IL-8, IL-10, IFN-γ) were quantified via ELISA. SENV-H was detected in 71.4% of CD patients on a GCD, 66.7% of those on a GFD, and only 19.0% of healthy controls. In contrast, SENV-D was found exclusively in healthy individuals (23.8%) and not in CD patients. CD patients on a GCD exhibited markedly elevated cytokine levels, particularly IL-6 (76.2 ± 12.3 pg/mL), IL-8 (112.0 ± 40.2 pg/mL), and IFN-γ (50.2 ± 15.2 pg/mL), compared to GFD patients and healthy controls. Multivariate logistic regression identified IL-1, IL-2, IL-4, IL-6, IL-8, and IL-10 as significantly associated with active disease (ORs < 1, p < 0.05). Strong to very strong positive correlations were observed between SENV-H positivity and cytokine levels, with IL-6, IL-8, and IL-1 each showing correlation coefficients around 0.99. These results suggest that SENV-H may play a role in promoting or amplifying mucosal immune responses in active CD, whereas SENV-D appears unrelated. The findings highlight a potential interaction between viral replication and immune activation in celiac disease, meriting further mechanistic investigation.</p> ]]> Cytokine-Mediated Modulation of SEN Virus Replication in Celiac Disease: Insights into Immune Signatures and Viral Persistence naseer ahmad bilal mehmood doi: 10.56578/hf030101 Healthcraft Frontiers 02-15-2025 Healthcraft Frontiers 02-15-2025 2025 3 1 Article 1 10.56578/hf030101 https://www.acadlore.com/article/HF/2025_3_1/hf030101 https://www.acadlore.com/article/HF/2024_2_4/hf020405 Brain tumors constitute a heterogeneous and life-threatening group of neurological disorders in which timely and accurate diagnosis is critical to improving patient outcomes. Conventional diagnostic practices, which rely heavily on manual interpretation of medical imaging, remain constrained by inter-observer variability, subjective judgment, and limited reproducibility, particularly when assigning tumor grades according to the World Health Organization (WHO) classification system. In recent years, machine learning (ML) and deep learning (DL) have emerged as transformative computational paradigms capable of automating complex pattern recognition in neuroimaging and enhancing diagnostic precision, efficiency, and consistency. A comprehensive review of ML/DL-based approaches for brain tumor analysis is presented in this study, encompassing key methodologies developed for tumor detection, segmentation, and classification across WHO grades. Despite notable research advances, clinical adoption remains impeded by several critical challenges, including insufficient dataset size and heterogeneity, a lack of model interpretability, limited generalizability across imaging acquisition protocols, and barriers associated with clinical integration and regulatory approval. Addressing these obstacles will require the development of large-scale, standardized, and multi-institutional datasets; the advancement of explainable artificial intelligence (XAI) frameworks to enhance clinical trust; and the incorporation of multi-modal patient data to improve diagnostic robustness. Furthermore, the convergence of ML/DL with emerging technologies such as blockchain and the Internet of Things (IoT) holds promise for enabling privacy-preserving, interoperable, and real-time diagnostic platforms. With continued advancements in algorithmic robustness, interpretability, and cross-institutional validation, ML/DL-based frameworks hold the potential to revolutionize brain tumor diagnosis and classification, ultimately improving diagnostic precision, prognostic assessment, and personalized treatment planning. 12-30-2024 <![CDATA[ Brain tumors constitute a heterogeneous and life-threatening group of neurological disorders in which timely and accurate diagnosis is critical to improving patient outcomes. Conventional diagnostic practices, which rely heavily on manual interpretation of medical imaging, remain constrained by inter-observer variability, subjective judgment, and limited reproducibility, particularly when assigning tumor grades according to the World Health Organization (WHO) classification system. In recent years, machine learning (ML) and deep learning (DL) have emerged as transformative computational paradigms capable of automating complex pattern recognition in neuroimaging and enhancing diagnostic precision, efficiency, and consistency. A comprehensive review of ML/DL-based approaches for brain tumor analysis is presented in this study, encompassing key methodologies developed for tumor detection, segmentation, and classification across WHO grades. Despite notable research advances, clinical adoption remains impeded by several critical challenges, including insufficient dataset size and heterogeneity, a lack of model interpretability, limited generalizability across imaging acquisition protocols, and barriers associated with clinical integration and regulatory approval. Addressing these obstacles will require the development of large-scale, standardized, and multi-institutional datasets; the advancement of explainable artificial intelligence (XAI) frameworks to enhance clinical trust; and the incorporation of multi-modal patient data to improve diagnostic robustness. Furthermore, the convergence of ML/DL with emerging technologies such as blockchain and the Internet of Things (IoT) holds promise for enabling privacy-preserving, interoperable, and real-time diagnostic platforms. With continued advancements in algorithmic robustness, interpretability, and cross-institutional validation, ML/DL-based frameworks hold the potential to revolutionize brain tumor diagnosis and classification, ultimately improving diagnostic precision, prognostic assessment, and personalized treatment planning. ]]> Machine Learning and Deep Learning for Brain Tumor Diagnosis and Classification: Methodologies, Challenges, and Future Directions sekar nurul fadilla rossi passarella doi: 10.56578/hf020405 Healthcraft Frontiers 12-30-2024 Healthcraft Frontiers 12-30-2024 2024 2 4 Article 225 10.56578/hf020405 https://www.acadlore.com/article/HF/2024_2_4/hf020405 https://www.acadlore.com/article/HF/2024_2_4/hf020404 Diabetic foot infection (DFI) represents a severe and potentially limb-threatening complication of long-standing and poorly controlled diabetes mellitus, a condition currently affecting over 422 million individuals globally and associated with more than 2 million annual deaths. This retrospective observational study was conducted at Hayatabad Medical Complex (HMC), Peshawar, with the objective of characterizing the clinical features, comorbidities, antibiotic regimens, and management outcomes of patients diagnosed with DFI. Clinical records of 341 patients admitted over a three-month period were reviewed. A male predominance was observed, with the highest prevalence noted among individuals aged 40–60 years. The majority of cases involved insulin-dependent diabetes mellitus, and an extended disease duration was identified as a major predisposing factor for DFI development. The mean hospitalization period was 25 days. Notably, complications such as peripheral neuropathy, diabetic nephropathy, and peripheral vasculopathy were more frequently documented in patients aged 65 years and older. Empirical treatment commonly involved poly-antibiotic regimens, which were administered in 64.81% of cases, underscoring the polymicrobial nature and severity of infections encountered. An amputation rate of 44.07% was recorded, which exceeds figures reported in comparable regional studies and is likely attributable to delayed clinical presentation and advanced stages of infection at the time of admission. The findings underscore the urgent need for enhanced early screening protocols, timely initiation of pathogen-targeted antimicrobial therapy, and multidisciplinary surgical intervention to reduce the risk of lower extremity amputation and the associated socio-economic burden. 12-30-2024 <![CDATA[ <p>Diabetic foot infection (DFI) represents a severe and potentially limb-threatening complication of long-standing and poorly controlled diabetes mellitus, a condition currently affecting over 422 million individuals globally and associated with more than 2 million annual deaths. This retrospective observational study was conducted at Hayatabad Medical Complex (HMC), Peshawar, with the objective of characterizing the clinical features, comorbidities, antibiotic regimens, and management outcomes of patients diagnosed with DFI. Clinical records of 341 patients admitted over a three-month period were reviewed. A male predominance was observed, with the highest prevalence noted among individuals aged 40–60 years. The majority of cases involved insulin-dependent diabetes mellitus, and an extended disease duration was identified as a major predisposing factor for DFI development. The mean hospitalization period was 25 days. Notably, complications such as peripheral neuropathy, diabetic nephropathy, and peripheral vasculopathy were more frequently documented in patients aged 65 years and older. Empirical treatment commonly involved poly-antibiotic regimens, which were administered in 64.81% of cases, underscoring the polymicrobial nature and severity of infections encountered. An amputation rate of 44.07% was recorded, which exceeds figures reported in comparable regional studies and is likely attributable to delayed clinical presentation and advanced stages of infection at the time of admission. The findings underscore the urgent need for enhanced early screening protocols, timely initiation of pathogen-targeted antimicrobial therapy, and multidisciplinary surgical intervention to reduce the risk of lower extremity amputation and the associated socio-economic burden.</p> ]]> Clinical Profile and Management of Diabetic Foot Infection at Hayatabad Medical Complex, Peshawar naseer ahmad bilal mehmood doi: 10.56578/hf020404 Healthcraft Frontiers 12-30-2024 Healthcraft Frontiers 12-30-2024 2024 2 4 Article 217 10.56578/hf020404 https://www.acadlore.com/article/HF/2024_2_4/hf020404 https://www.acadlore.com/article/HF/2024_2_4/hf020403 Alzheimer’s disease (AD), a progressive neurodegenerative disorder characterized by severe cognitive decline, necessitates early and accurate diagnosis to improve patient outcomes. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), have demonstrated significant potential in medical image analysis (MIA). This study presents a robust CNN-based framework for the classification of AD using magnetic resonance imaging (MRI) data. The proposed methodology incorporates contrast stretching for image preprocessing, followed by principal component analysis (PCA) and recursive feature elimination (RFE) for feature selection, to enhance the discriminative power of the model. The framework is designed to classify MRI into four distinct categories: non-demented, very mildly demented, mildly demented, and moderately demented. Experimental validation on a comprehensive dataset reveals that the proposed approach achieves exceptional performance, with a validation accuracy of 97% and a training accuracy of 100%, alongside reduced loss and improved sensitivity. The integration of PCA and RFE is shown to effectively reduce dimensionality while retaining diagnostically critical features, thereby optimizing the model’s efficiency and interpretability. These findings underscore the potential of DL techniques to revolutionize the early detection and diagnosis of AD, offering a powerful tool for clinical decision-making and advancing the field of neuroimaging analysis. The proposed framework not only addresses the challenges of high-dimensional data but also provides a scalable and generalizable solution for the classification of neurodegenerative disorders. 12-30-2024 <![CDATA[ <p>Alzheimer’s disease (AD), a progressive neurodegenerative disorder characterized by severe cognitive decline, necessitates early and accurate diagnosis to improve patient outcomes. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), have demonstrated significant potential in medical image analysis (MIA). This study presents a robust CNN-based framework for the classification of AD using magnetic resonance imaging (MRI) data. The proposed methodology incorporates contrast stretching for image preprocessing, followed by principal component analysis (PCA) and recursive feature elimination (RFE) for feature selection, to enhance the discriminative power of the model. The framework is designed to classify MRI into four distinct categories: non-demented, very mildly demented, mildly demented, and moderately demented. Experimental validation on a comprehensive dataset reveals that the proposed approach achieves exceptional performance, with a validation accuracy of 97% and a training accuracy of 100%, alongside reduced loss and improved sensitivity. The integration of PCA and RFE is shown to effectively reduce dimensionality while retaining diagnostically critical features, thereby optimizing the model’s efficiency and interpretability. These findings underscore the potential of DL techniques to revolutionize the early detection and diagnosis of AD, offering a powerful tool for clinical decision-making and advancing the field of neuroimaging analysis. The proposed framework not only addresses the challenges of high-dimensional data but also provides a scalable and generalizable solution for the classification of neurodegenerative disorders.</p> ]]> Deep Learning-Based MRI Classification for Early Diagnosis of Alzheimer’s Disease seyyed ahmad edalatpanah shamila saeedi nadia ghasemabadi doi: 10.56578/hf020403 Healthcraft Frontiers 12-30-2024 Healthcraft Frontiers 12-30-2024 2024 2 4 Article 203 10.56578/hf020403 https://www.acadlore.com/article/HF/2024_2_4/hf020403 https://www.acadlore.com/article/HF/2024_2_4/hf020402 Brain tumors represent a critical medical condition where early and accurate detection is paramount for effective treatment and improved patient outcomes. Traditional diagnostic methods relying on Magnetic Resonance Imaging (MRI) are often labor-intensive, time-consuming, and susceptible to human error, underscoring the need for more reliable and efficient approaches. In this study, a novel deep learning (DL) framework based on the Densely Connected Convolutional Network (DenseNet) architecture is proposed for the automated classification of brain tumors, aiming to enhance diagnostic precision and streamline medical image analysis. The framework incorporates adaptive filtering for noise reduction, Mask Region-based Convolutional Neural Network (Mask R-CNN) for precise tumor segmentation, and Gray Level Co-occurrence Matrix (GLCM) for robust feature extraction. The DenseNet architecture is employed to classify brain tumors into four categories: gliomas, meningiomas, pituitary tumors, and non-tumor cases. The model is trained and evaluated using the Kaggle MRI dataset, achieving a state-of-the-art classification accuracy of 96.96%. Comparative analyses demonstrate that the proposed framework outperforms traditional methods, including Back Propagation (BP), U-Net, and Recurrent Convolutional Neural Network (RCNN), in terms of sensitivity, specificity, and precision. The experimental results highlight the potential of integrating advanced DL techniques with medical image processing to significantly improve diagnostic accuracy and efficiency. This study not only provides a robust and reliable solution for brain tumor detection but also underscores the transformative impact of DL in medical imaging, offering radiologists a powerful tool for faster and more accurate diagnosis. 12-30-2024 <![CDATA[ <p>Brain tumors represent a critical medical condition where early and accurate detection is paramount for effective treatment and improved patient outcomes. Traditional diagnostic methods relying on Magnetic Resonance Imaging (MRI) are often labor-intensive, time-consuming, and susceptible to human error, underscoring the need for more reliable and efficient approaches. In this study, a novel deep learning (DL) framework based on the Densely Connected Convolutional Network (DenseNet) architecture is proposed for the automated classification of brain tumors, aiming to enhance diagnostic precision and streamline medical image analysis. The framework incorporates adaptive filtering for noise reduction, Mask Region-based Convolutional Neural Network (Mask R-CNN) for precise tumor segmentation, and Gray Level Co-occurrence Matrix (GLCM) for robust feature extraction. The DenseNet architecture is employed to classify brain tumors into four categories: gliomas, meningiomas, pituitary tumors, and non-tumor cases. The model is trained and evaluated using the Kaggle MRI dataset, achieving a state-of-the-art classification accuracy of 96.96%. Comparative analyses demonstrate that the proposed framework outperforms traditional methods, including Back Propagation (BP), U-Net, and Recurrent Convolutional Neural Network (RCNN), in terms of sensitivity, specificity, and precision. The experimental results highlight the potential of integrating advanced DL techniques with medical image processing to significantly improve diagnostic accuracy and efficiency. This study not only provides a robust and reliable solution for brain tumor detection but also underscores the transformative impact of DL in medical imaging, offering radiologists a powerful tool for faster and more accurate diagnosis.</p> ]]> A DenseNet-Based Deep Learning Framework for Automated Brain Tumor Classification soheil fakheri mohammadreza yamaghani azamossadat nourbakhsh doi: 10.56578/hf020402 Healthcraft Frontiers 12-30-2024 Healthcraft Frontiers 12-30-2024 2024 2 4 Article 188 10.56578/hf020402 https://www.acadlore.com/article/HF/2024_2_4/hf020402 https://www.acadlore.com/article/HF/2024_2_4/hf020401 Diabetes is a chronic metabolic disorder that affects millions of people worldwide, making early detection crucial for effective management. This study assesses the effectiveness of three machine learning (ML) models, Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT), in predicting diabetes based on data from 392 individuals, including their demographic and clinical characteristics. The dataset underwent preprocessing to maintain data integrity, was standardized for model compatibility, and analyzed through feature correlation heatmaps, feature importance assessments, and statistical significance tests. The findings revealed that LR surpassed the other models, with the highest accuracy (78%), precision (73%), and F1-score (65%) for diabetic cases. NB showed moderate performance with 75% accuracy, while DT demonstrated the lowest accuracy (71%) due to overfitting. Receiver Operating Characteristic (ROC) analysis revealed strong discriminative power across all models, although perfect Area Under the Curve (AUC) scores indicate potential overfitting needing further validation. The study emphasizes the significance of key features like Glucose, Body Mass Index (BMI), and Age, which showed notable differences between diabetic and non-diabetic individuals. By enabling early detection and proactive management, these models can contribute to reducing diabetes-related complications, enhancing patient outcomes, and lessening the burden on healthcare systems. Future research should investigate ensemble learning, deep learning, and real-time data integration from Internet of Things (IoT) devices to improve predictive accuracy and scalability. 12-30-2024 <![CDATA[ <p>Diabetes is a chronic metabolic disorder that affects millions of people worldwide, making early detection crucial for effective management. This study assesses the effectiveness of three machine learning (ML) models, Logistic Regression (LR), Naïve Bayes (NB), and Decision Tree (DT), in predicting diabetes based on data from 392 individuals, including their demographic and clinical characteristics. The dataset underwent preprocessing to maintain data integrity, was standardized for model compatibility, and analyzed through feature correlation heatmaps, feature importance assessments, and statistical significance tests. The findings revealed that LR surpassed the other models, with the highest accuracy (78%), precision (73%), and F1-score (65%) for diabetic cases. NB showed moderate performance with 75% accuracy, while DT demonstrated the lowest accuracy (71%) due to overfitting. Receiver Operating Characteristic (ROC) analysis revealed strong discriminative power across all models, although perfect Area Under the Curve (AUC) scores indicate potential overfitting needing further validation. The study emphasizes the significance of key features like Glucose, Body Mass Index (BMI), and Age, which showed notable differences between diabetic and non-diabetic individuals. By enabling early detection and proactive management, these models can contribute to reducing diabetes-related complications, enhancing patient outcomes, and lessening the burden on healthcare systems. Future research should investigate ensemble learning, deep learning, and real-time data integration from Internet of Things (IoT) devices to improve predictive accuracy and scalability.</p> ]]> Machine Learning for Diabetes Prediction: Performance Analysis Using Logistic Regression, Naïve Bayes, and Decision Tree Models rupinder kaur raman kumar swapandeep kaur gurneet singh arshnoor kaur sukhpal singh doi: 10.56578/hf020401 Healthcraft Frontiers 12-30-2024 Healthcraft Frontiers 12-30-2024 2024 2 4 Article 169 10.56578/hf020401 https://www.acadlore.com/article/HF/2024_2_4/hf020401 https://www.acadlore.com/article/HF/2024_2_3/hf020305 Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes. Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency. Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities. This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN). Patient data were stratified by age group and gender to assess the model’s performance across diverse demographic profiles. Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy. Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%. These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management. 09-29-2024 <![CDATA[ <p>Heart disease remains a leading cause of mortality worldwide, necessitating early and accurate detection to improve clinical outcomes. Traditional diagnostic approaches relying on conventional clinical data analysis often encounter limitations in precision and efficiency. Machine learning (ML) techniques offer a promising solution by enhancing predictive accuracy and decision-making capabilities. This study evaluates the performance of a clinical support system (CSS) for heart disease prediction using a hybrid classification approach that integrates support vector machine (SVM) and k-nearest neighbor (KNN). Patient data were stratified by age group and gender to assess the model’s performance across diverse demographic profiles. Key performance metrics, including accuracy, recall, precision, F1-score, and area under the curve (AUC), were employed to quantify predictive efficacy. Experimental results demonstrated that the combined SVM-KNN model achieved superior classification performance, yielding an accuracy of 97.2%, recall of 97.6%, precision of 96.8%, AUC of 97.1%, and an F1-score of 98.2%. These findings indicate that the integration of SVM and KNN enhances heart disease prediction accuracy, thereby reinforcing the potential of CSS in improving early diagnosis and patient management.</p> ]]> Performance Assessment of a Clinical Support System for Heart Disease Prediction Using Machine Learning koteswara rao kodepogu eswar patnala jagadeeswara rao annam shobana gorintla veerla vijaya rama krishna vipparla aruna vijaya bharathi manjeti doi: 10.56578/hf020305 Healthcraft Frontiers 09-29-2024 Healthcraft Frontiers 09-29-2024 2024 2 3 Article 159 10.56578/hf020305 https://www.acadlore.com/article/HF/2024_2_3/hf020305 https://www.acadlore.com/article/HF/2024_2_3/hf020304 A bibliometric analysis was conducted to explore the research trends and emerging hotspots in the application of health monitoring technologies within nursing. Literature spanning from January 2021 to January 2025 was retrieved from the Web of Science Core Collection (WOSCC), and CiteSpace software was employed to analyze and visualize research outputs, institutional contributions, author collaborations, high-frequency keywords, and the evolution of keyword clusters over time. A total of 425 articles were identified, revealing a stable global publication output. The United States emerged as the leading contributor, with 138 articles, followed by China with 47. Prominent keywords such as "care," "management," and "remote patient monitoring (RPM)" were found to be indicative of current research foci. Analysis indicates a shift towards home-based care, smartphone integration, digital health solutions, and wearable devices, particularly in managing clinical conditions such as cardiovascular disease (CVD), cancer, and diabetes. The prevailing research trends highlight the importance of remote monitoring and nursing care within home settings, with an increasing emphasis on chronic diseases. Despite the growth in research activity, uneven international development and limited collaborative efforts, primarily within research teams, present challenges to the field’s progress. It is suggested that future research should focus on fostering international collaboration between academic, healthcare, and engineering sectors to ensure that monitoring technologies align with clinical needs. Moreover, the establishment of international regulations was recommended to standardize production processes, enhance product reliability, and facilitate the broader application of these technologies in nursing practice. 09-29-2024 <![CDATA[ <p>A bibliometric analysis was conducted to explore the research trends and emerging hotspots in the application of health monitoring technologies within nursing. Literature spanning from January 2021 to January 2025 was retrieved from the Web of Science Core Collection (WOSCC), and CiteSpace software was employed to analyze and visualize research outputs, institutional contributions, author collaborations, high-frequency keywords, and the evolution of keyword clusters over time. A total of 425 articles were identified, revealing a stable global publication output. The United States emerged as the leading contributor, with 138 articles, followed by China with 47. Prominent keywords such as "care," "management," and "remote patient monitoring (RPM)" were found to be indicative of current research foci. Analysis indicates a shift towards home-based care, smartphone integration, digital health solutions, and wearable devices, particularly in managing clinical conditions such as cardiovascular disease (CVD), cancer, and diabetes. The prevailing research trends highlight the importance of remote monitoring and nursing care within home settings, with an increasing emphasis on chronic diseases. Despite the growth in research activity, uneven international development and limited collaborative efforts, primarily within research teams, present challenges to the field’s progress. It is suggested that future research should focus on fostering international collaboration between academic, healthcare, and engineering sectors to ensure that monitoring technologies align with clinical needs. Moreover, the establishment of international regulations was recommended to standardize production processes, enhance product reliability, and facilitate the broader application of these technologies in nursing practice.</p> ]]> Emerging Trends and Hotspots in Health Monitoring Technologies for Nursing: A Bibliometric Analysis yuxuan cui yunhan shao han shi jiaye qian jing kang kangnan bao lemin fang wangxu yang dunchun yang junyan zhao shihua cao doi: 10.56578/hf020304 Healthcraft Frontiers 09-29-2024 Healthcraft Frontiers 09-29-2024 2024 2 3 Article 150 10.56578/hf020304 https://www.acadlore.com/article/HF/2024_2_3/hf020304 https://www.acadlore.com/article/HF/2024_2_3/hf020303 The increasing global population has led to a corresponding rise in the demand for blood in healthcare settings, necessitating the development of efficient and transparent blood management systems. The process of blood donation and transfusion is critical to public health and patient well-being, requiring robust systems to ensure safety, reliability, and traceability. This study proposes a blockchain-based blood donation system designed to enhance transparency, accountability, and privacy in both the donation and transfusion processes. Blockchain technology, with its inherent capabilities for secure and decentralized record-keeping, offers a solution to the challenges of maintaining confidentiality, particularly in relation to the sensitive personal information of both donors and recipients. The adoption of blockchain also facilitates a more sustainable approach to blood donation management, promoting the optimization of resources and reduction of waste, which contributes to environmental sustainability in the healthcare sector. The integration of blockchain within blood donation processes is expected to not only improve operational transparency but also support the broader goals of sustainability by reducing carbon footprints associated with resource management and logistics. This study outlines the design of such a system, highlighting its potential benefits in terms of improving system reliability, protecting sensitive data, and enhancing the sustainability of healthcare operations. 09-29-2024 <![CDATA[ The increasing global population has led to a corresponding rise in the demand for blood in healthcare settings, necessitating the development of efficient and transparent blood management systems. The process of blood donation and transfusion is critical to public health and patient well-being, requiring robust systems to ensure safety, reliability, and traceability. This study proposes a blockchain-based blood donation system designed to enhance transparency, accountability, and privacy in both the donation and transfusion processes. Blockchain technology, with its inherent capabilities for secure and decentralized record-keeping, offers a solution to the challenges of maintaining confidentiality, particularly in relation to the sensitive personal information of both donors and recipients. The adoption of blockchain also facilitates a more sustainable approach to blood donation management, promoting the optimization of resources and reduction of waste, which contributes to environmental sustainability in the healthcare sector. The integration of blockchain within blood donation processes is expected to not only improve operational transparency but also support the broader goals of sustainability by reducing carbon footprints associated with resource management and logistics. This study outlines the design of such a system, highlighting its potential benefits in terms of improving system reliability, protecting sensitive data, and enhancing the sustainability of healthcare operations. ]]> A Blockchain-Based Blood Donation System: Enhancing Transparency, Accountability, and Sustainability in Healthcare arzu sevinç fatih özyurt doi: 10.56578/hf020303 Healthcraft Frontiers 09-29-2024 Healthcraft Frontiers 09-29-2024 2024 2 3 Article 139 10.56578/hf020303 https://www.acadlore.com/article/HF/2024_2_3/hf020303 https://www.acadlore.com/article/HF/2024_2_3/hf020302 Electromyographic (EMG) analysis was conducted to evaluate the functional characteristics of masticatory muscles in patients with myogenous temporomandibular disorders (TMD), aiming to enhance the clinical understanding of muscle activity in these conditions. Based on the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), 28 patients with myogenous TMD, characterized by persistent pain exceeding six months, were examined alongside a control group of 35 asymptomatic subjects. EMG assessments were performed on the masseter, temporalis, and suprahyoid muscles during resting states and maximum intercuspation clench. Quantitative parameters, including myoelectric indices in the amplitude domain and mean power frequency (MPF) in the frequency domain, were evaluated. Significant differences in muscle activity patterns between the TMD and control groups were observed. During maximum clenching, temporalis muscles (TA) in TMD patients exhibited a markedly higher asymmetry index and activity index, alongside a lower MPF, compared to the control group. Conversely, the MPF of the suprahyoid muscles was elevated, while masseter muscles (MM) displayed a reduction in MPF. In the resting state, the MPF of the TA was found to be higher than that of both the control group and the MM. These findings indicate that patients with myogenous TMD exhibit increased muscle activity asymmetry, reduced coordination, and altered frequency-domain characteristics of the masticatory muscles. The results suggest that the TA may play a more significant role in the compensatory mechanisms associated with myogenous TMD, potentially contributing to the observed dysfunction and pain. This study underscores the utility of EMG as a diagnostic tool for elucidating the pathophysiological changes in masticatory muscle function in TMD and highlights the potential for targeted therapeutic interventions based on these findings. 09-29-2024 <![CDATA[ <p>Electromyographic (EMG) analysis was conducted to evaluate the functional characteristics of masticatory muscles in patients with myogenous temporomandibular disorders (TMD), aiming to enhance the clinical understanding of muscle activity in these conditions. Based on the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), 28 patients with myogenous TMD, characterized by persistent pain exceeding six months, were examined alongside a control group of 35 asymptomatic subjects. EMG assessments were performed on the masseter, temporalis, and suprahyoid muscles during resting states and maximum intercuspation clench. Quantitative parameters, including myoelectric indices in the amplitude domain and mean power frequency (MPF) in the frequency domain, were evaluated. Significant differences in muscle activity patterns between the TMD and control groups were observed. During maximum clenching, temporalis muscles (TA) in TMD patients exhibited a markedly higher asymmetry index and activity index, alongside a lower MPF, compared to the control group. Conversely, the MPF of the suprahyoid muscles was elevated, while masseter muscles (MM) displayed a reduction in MPF. In the resting state, the MPF of the TA was found to be higher than that of both the control group and the MM. These findings indicate that patients with myogenous TMD exhibit increased muscle activity asymmetry, reduced coordination, and altered frequency-domain characteristics of the masticatory muscles. The results suggest that the TA may play a more significant role in the compensatory mechanisms associated with myogenous TMD, potentially contributing to the observed dysfunction and pain. This study underscores the utility of EMG as a diagnostic tool for elucidating the pathophysiological changes in masticatory muscle function in TMD and highlights the potential for targeted therapeutic interventions based on these findings.</p> ]]> Electromyographic Analysis of Masticatory Muscle Function in Patients with Myogenous Temporomandibular Disorders jin liu tianjun liu yongping ma doi: 10.56578/hf020302 Healthcraft Frontiers 09-29-2024 Healthcraft Frontiers 09-29-2024 2024 2 3 Article 130 10.56578/hf020302 https://www.acadlore.com/article/HF/2024_2_3/hf020302 https://www.acadlore.com/article/HF/2024_2_3/hf020301 A two-month prospective study conducted at Hayatabad Medical Complex (HMC) Peshawar, Pakistan. In this study the pharmacotherapy patterns and drug-drug interaction (DDI) incidences were analyzed among 150 diabetic patients, of whom 50 presented with diabetic foot ulcer (DFU). Significant deviations from World Health Organization (WHO) core prescribing indicators were observed, particularly in the areas of polypharmacy and generic prescribing practices. The majority of DFU patients were from urban regions, with sedentary lifestyle factors identified as prominent contributors to DFU development. A higher incidence of DFU was noted among male patients with type 2 diabetes mellitus (T2DM) compared to female patients. Age distribution analysis revealed that patient ages ranged from 8 to 85 years, with 68% falling within the 41-60 age bracket, while only 2% were under 20 years of age. Among the all 391 pharmacotherapeutic agents prescribed, injectable medications constituted the majority (47.82%). Analysis of DDIs showed that 39.1% of prescribed medications were associated with drug interactions, with 72% of these classified as major interactions. The most frequently observed major DDIs involved combinations such as aspirin with Ramipril and Pregabalin with Losartan. These findings highlight the necessity for clinical pharmacists to review prescribing regimens to mitigate the risk of severe DDIs. The high prevalence of diabetes and DFU in this patient cohort is closely associated with lifestyle factors, insufficient health education, and lack of physical activity. These findings underline the urgent need for preventative strategies, including lifestyle modifications and public health education. Further investigation is recommended to enhance understanding of DFU risk factors and to develop improved prognostic and preventive frameworks. 09-29-2024 <![CDATA[ <p>A two-month prospective study conducted at Hayatabad Medical Complex (HMC) Peshawar, Pakistan. In this study the pharmacotherapy patterns and drug-drug interaction (DDI) incidences were analyzed among 150 diabetic patients, of whom 50 presented with diabetic foot ulcer (DFU). Significant deviations from World Health Organization (WHO) core prescribing indicators were observed, particularly in the areas of polypharmacy and generic prescribing practices. The majority of DFU patients were from urban regions, with sedentary lifestyle factors identified as prominent contributors to DFU development. A higher incidence of DFU was noted among male patients with type 2 diabetes mellitus (T2DM) compared to female patients. Age distribution analysis revealed that patient ages ranged from 8 to 85 years, with 68% falling within the 41-60 age bracket, while only 2% were under 20 years of age. Among the all 391 pharmacotherapeutic agents prescribed, injectable medications constituted the majority (47.82%). Analysis of DDIs showed that 39.1% of prescribed medications were associated with drug interactions, with 72% of these classified as major interactions. The most frequently observed major DDIs involved combinations such as aspirin with Ramipril and Pregabalin with Losartan. These findings highlight the necessity for clinical pharmacists to review prescribing regimens to mitigate the risk of severe DDIs. The high prevalence of diabetes and DFU in this patient cohort is closely associated with lifestyle factors, insufficient health education, and lack of physical activity. These findings underline the urgent need for preventative strategies, including lifestyle modifications and public health education. Further investigation is recommended to enhance understanding of DFU risk factors and to develop improved prognostic and preventive frameworks.</p> ]]> Pharmacotherapy Patterns of Diabetic Foot Ulcer Patients at Hayatabad Medical Complex, Peshawar, Pakistan: A Prospective Study farooq khan abid ullah doi: 10.56578/hf020301 Healthcraft Frontiers 09-29-2024 Healthcraft Frontiers 09-29-2024 2024 2 3 Article 118 10.56578/hf020301 https://www.acadlore.com/article/HF/2024_2_3/hf020301 https://www.acadlore.com/article/HF/2024_2_2/hf020205 Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases, an increased risk of suicide. Affecting approximately 264 million individuals worldwide across diverse age groups, depression necessitates effective and timely detection for intervention. In primary healthcare, the Patient Health Questionnaire-9 (PHQ-9) serves as a crucial tool for screening depression. This study leverages the PHQ-9 dataset, comprising 12 features and 534 samples, to evaluate depression levels using advanced machine learning (ML) techniques. A comparative analysis of the Support Vector Classifier (SVC) and AdaBoost Classifier (ABC) was conducted to determine their efficacy in classifying depression severity on a scale from 0 to 4. The SVC emerged as the superior model, achieving an accuracy of 94%. This research contributes to the early detection and prevention of depression by proposing an interactive interface designed to enhance user engagement. Future work will focus on expanding the dataset to improve model generalization and robustness, thereby facilitating more accurate and widespread applications in clinical settings. 06-29-2024 <![CDATA[ <p>Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases, an increased risk of suicide. Affecting approximately 264 million individuals worldwide across diverse age groups, depression necessitates effective and timely detection for intervention. In primary healthcare, the Patient Health Questionnaire-9 (PHQ-9) serves as a crucial tool for screening depression. This study leverages the PHQ-9 dataset, comprising 12 features and 534 samples, to evaluate depression levels using advanced machine learning (ML) techniques. A comparative analysis of the Support Vector Classifier (SVC) and AdaBoost Classifier (ABC) was conducted to determine their efficacy in classifying depression severity on a scale from 0 to 4. The SVC emerged as the superior model, achieving an accuracy of 94%. This research contributes to the early detection and prevention of depression by proposing an interactive interface designed to enhance user engagement. Future work will focus on expanding the dataset to improve model generalization and robustness, thereby facilitating more accurate and widespread applications in clinical settings.</p> ]]> Unlocking Minds: An Adaptive Machine Learning Approach for Early Detection of Depression hafiz burhan ul haq muhammad nauman irshad muhammad daniyal baig doi: 10.56578/hf020205 Healthcraft Frontiers 06-29-2024 Healthcraft Frontiers 06-29-2024 2024 2 2 Article 107 10.56578/hf020205 https://www.acadlore.com/article/HF/2024_2_2/hf020205 https://www.acadlore.com/article/HF/2024_2_2/hf020204 This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles. 06-29-2024 <![CDATA[ <p style="text-align: justify">This comprehensive review investigates the ethical implications of artificial intelligence (AI)-driven predictive analytics in healthcare, with a focus on patient privacy, algorithmic bias, equitable access, and transparency. The study further explores the integration of these ethical considerations into educational frameworks to enhance the training and preparedness of healthcare professionals in the responsible use of AI technologies. A systematic literature review was conducted using databases such as PubMed, Scopus, and Google Scholar, employing keywords related to AI, predictive analytics, healthcare, education, and ethics. Articles published from 2017 onwards, discussing the ethical challenges and applications of AI in healthcare and educational settings, were included. Thematic analysis of selected articles revealed significant ethical concerns, including patient privacy, algorithmic bias, and equitable access to AI technologies. Findings underscored the necessity for robust data protection mechanisms, transparent algorithm development, and equitable access policies. The study also highlighted the importance of incorporating AI literacy and ethical training in medical education. An ethical framework was proposed, outlining strategies to address these challenges in both healthcare practice and educational curricula. This framework aims to ensure the responsible use of AI technologies, promote transparency, and mitigate biases in healthcare settings. By addressing a critical gap in understanding the ethical implications of AI-driven predictive analytics in healthcare and its integration into education, the study contributes to the development of guidelines and policies for the equitable and transparent deployment of AI. The proposed ethical framework provides actionable recommendations for stakeholders, aiming to enhance medical education and improve patient outcomes while upholding essential ethical principles.</p> ]]> Ethical Implications and Educational Integration of AI-Driven Predictive Analytics in Healthcare: A Comprehensive Review askar garad budiman al iman halim purnomo doi: 10.56578/hf020204 Healthcraft Frontiers 06-29-2024 Healthcraft Frontiers 06-29-2024 2024 2 2 Article 97 10.56578/hf020204 https://www.acadlore.com/article/HF/2024_2_2/hf020204 https://www.acadlore.com/article/HF/2024_2_2/hf020203 In this cross-sectional study, the prevalence and characteristics of adverse effects following the administration of the third dose of the coronavirus disease 2019 (COVID-19) vaccines were compared between recipients in Palestine and Jordan. Data were collected via an online survey targeting random samples from both countries. In Palestine, the primary factors predisposing individuals to side effects after the third dose were prior adverse reactions to earlier vaccinations and a history of COVID-19 infection before vaccination. Minor contributing factors included food sensitivities, weight, and drug sensitivities. In Jordan, gender, smoking, and food sensitivities emerged as the most significant variables influencing the development of side effects, with age being a secondary factor. Weight, COVID-19 infection post-vaccination, and prior adverse reactions to earlier doses were less significant. In Palestine, individuals with diabetes and respiratory diseases were more prone to adverse effects, followed by those who are obese, and those with cardiovascular diseases, osteoporosis, thyroid disorders, immune diseases, cancer, arthritis, and hypertension. In Jordan, participants with arthritis were the most likely to develop side effects, followed by those who are obese, and those with respiratory conditions and thyroid disorders. These findings confirm that COVID-19 vaccines authorized for use are generally safe, and vaccination remains a crucial tool in curbing the spread of the virus. Acceptance of the third dose has been notable in both Palestine and Jordan, underscoring the value of booster doses in enhancing immunity. 06-05-2024 <![CDATA[ <p>In this cross-sectional study, the prevalence and characteristics of adverse effects following the administration of the third dose of the coronavirus disease 2019 (COVID-19) vaccines were compared between recipients in Palestine and Jordan. Data were collected via an online survey targeting random samples from both countries. In Palestine, the primary factors predisposing individuals to side effects after the third dose were prior adverse reactions to earlier vaccinations and a history of COVID-19 infection before vaccination. Minor contributing factors included food sensitivities, weight, and drug sensitivities. In Jordan, gender, smoking, and food sensitivities emerged as the most significant variables influencing the development of side effects, with age being a secondary factor. Weight, COVID-19 infection post-vaccination, and prior adverse reactions to earlier doses were less significant. In Palestine, individuals with diabetes and respiratory diseases were more prone to adverse effects, followed by those who are obese, and those with cardiovascular diseases, osteoporosis, thyroid disorders, immune diseases, cancer, arthritis, and hypertension. In Jordan, participants with arthritis were the most likely to develop side effects, followed by those who are obese, and those with respiratory conditions and thyroid disorders. These findings confirm that COVID-19 vaccines authorized for use are generally safe, and vaccination remains a crucial tool in curbing the spread of the virus. Acceptance of the third dose has been notable in both Palestine and Jordan, underscoring the value of booster doses in enhancing immunity.</p> ]]> A Comparative Analysis of Side Effects from the Third Dose of COVID-19 Vaccines in Palestine and Jordan jebril al-hrinat aseel hendi abdullah m. al-ansi doi: 10.56578/hf020203 Healthcraft Frontiers 06-05-2024 Healthcraft Frontiers 06-05-2024 2024 2 2 Article 79 10.56578/hf020203 https://www.acadlore.com/article/HF/2024_2_2/hf020203 https://www.acadlore.com/article/HF/2024_2_2/hf020202 A retrospective analysis was conducted to assess potential drug-drug interactions (pDDIs) in the management of cardiovascular diseases, evaluating 500 prescriptions from hospitalized patients between January 1 and April 1, 2023. Using Medscape online software for the identification of drug-drug interactions (DDIs) and SPSS version 21 for statistical analysis, the study documented a 93% occurrence rate of pDDIs across the prescriptions. These interactions were categorized as serious (15% of cases, n=760, maximum per encounter: 4, mean: 1.52 ± 1.064), significant (75.6% of cases, n=3855, maximum per encounter: 30, mean: 7.71 ± 4.583), and minor (9.5% of cases, n=485, maximum per encounter: 4, mean: 0.95 ± 1.025). On average, 9.5 medications were prescribed per patient. Factors significantly associated with the incidence of pDDIs included age (r= 0.921, P 06-04-2024 <![CDATA[ <p style="text-align: justify">A retrospective analysis was conducted to assess potential drug-drug interactions (pDDIs) in the management of cardiovascular diseases, evaluating 500 prescriptions from hospitalized patients between January 1 and April 1, 2023. Using Medscape online software for the identification of drug-drug interactions (DDIs) and SPSS version 21 for statistical analysis, the study documented a 93% occurrence rate of pDDIs across the prescriptions. These interactions were categorized as serious (15% of cases, n=760, maximum per encounter: 4, mean: 1.52 ± 1.064), significant (75.6% of cases, n=3855, maximum per encounter: 30, mean: 7.71 ± 4.583), and minor (9.5% of cases, n=485, maximum per encounter: 4, mean: 0.95 ± 1.025). On average, 9.5 medications were prescribed per patient. Factors significantly associated with the incidence of pDDIs included age (r= 0.921, P < 0.01), presence of concurrent diseases (r= 0.782, P < 0.01), length of hospital stay (r= 0.559, P < 0.01), and the number of prescribed drugs (r= 0.472, P < 0.01). The most frequent interacting combinations were identified, with Clopidogrel + Enoxaparin (38.15%, n=290) and Enoxaparin + Aspirin (26.92%, n=210) being the most common, followed by other notable combinations. The study recorded adverse drug reactions in 15 patients. This investigation highlights a significant prevalence of pDDIs, particularly in cases of polypharmacy among cardiovascular patients. It underscores the critical need for systematic analysis and vigilant monitoring of prescriptions prior to drug administration by healthcare professionals.</p> ]]> Evaluation of Factors Contributing to Potential Drug-Drug Interactions in Cardiovascular Disease Management: A Retrospective Study awais khan haya hussain doi: 10.56578/hf020202 Healthcraft Frontiers 06-04-2024 Healthcraft Frontiers 06-04-2024 2024 2 2 Article 68 10.56578/hf020202 https://www.acadlore.com/article/HF/2024_2_2/hf020202 https://www.acadlore.com/article/HF/2024_2_2/hf020201 Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Regency, has reported a notable burden of the disease, evidenced by 102 confirmed cases in 2022 as per the annual parasite incidence (API) data, highlighting indigenous transmissions in specific locales. This research was conducted to assess the susceptibility to malaria within the operational area of the Hila Perawatan Primary Healthcare Centre (Puskesmas), situated in the Leihitu sub-district of Ambon Island, through an examination of $Anopheles$ mosquito breeding sites, larval densities, and habitat indices. Employing a descriptive research design, this cross-sectional observational study was carried out on October 26-27, 2023, to meticulously document the ecological footprint of the $Anopheles$ mosquito, particularly $Anopheles$ $farauti$. Findings reveal a habitat index (HI) of 33% in Kaitetu village with a larval density of 20%, indicating a significant presence of Anopheles farauti larvae. These findings suggest that environmental and behavioral factors within households, such as the use of gauze and ceilings, nocturnal activities, application of mosquito repellents, wearing of long-sleeved clothing, and utilization of mosquito nets, are pivotal in influencing malaria transmission dynamics. This study underscores the imperative of integrating environmental management with community engagement strategies to mitigate malaria transmission in endemic regions. The results not only provide a nuanced understanding of the $Anopheles$ mosquito's breeding patterns and its implications for malaria transmission but also offer a foundational basis for tailoring targeted interventions aimed at reducing the malaria burden in the Central Maluku District. 05-07-2024 <![CDATA[ <p style="text-align: justify">Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Regency, has reported a notable burden of the disease, evidenced by 102 confirmed cases in 2022 as per the annual parasite incidence (API) data, highlighting indigenous transmissions in specific locales. This research was conducted to assess the susceptibility to malaria within the operational area of the Hila Perawatan Primary Healthcare Centre (Puskesmas), situated in the Leihitu sub-district of Ambon Island, through an examination of $Anopheles$ mosquito breeding sites, larval densities, and habitat indices. Employing a descriptive research design, this cross-sectional observational study was carried out on October 26-27, 2023, to meticulously document the ecological footprint of the $Anopheles$ mosquito, particularly $Anopheles$ $farauti$. Findings reveal a habitat index (HI) of 33% in Kaitetu village with a larval density of 20%, indicating a significant presence of Anopheles farauti larvae. These findings suggest that environmental and behavioral factors within households, such as the use of gauze and ceilings, nocturnal activities, application of mosquito repellents, wearing of long-sleeved clothing, and utilization of mosquito nets, are pivotal in influencing malaria transmission dynamics. This study underscores the imperative of integrating environmental management with community engagement strategies to mitigate malaria transmission in endemic regions. The results not only provide a nuanced understanding of the $Anopheles$ mosquito's breeding patterns and its implications for malaria transmission but also offer a foundational basis for tailoring targeted interventions aimed at reducing the malaria burden in the Central Maluku District.</p> ]]> Investigating Malaria Susceptibility in Central Maluku District: A Focus on $Anopheles$ Mosquito Habitats yura witsqa firmansyah adi anggoro parulian hedie kristiawan bhisma jaya prasaja elanda fikri linda yanti juliana noya doi: 10.56578/hf020201 Healthcraft Frontiers 05-07-2024 Healthcraft Frontiers 05-07-2024 2024 2 2 Article 59 10.56578/hf020201 https://www.acadlore.com/article/HF/2024_2_2/hf020201 https://www.acadlore.com/article/HF/2024_2_1/hf020105 Tuberculosis (TB), an airborne disease caused by Mycobacterium, poses a significant global health challenge due to its rapid transmission through air and interaction with infected individuals. This study presents a comprehensive dynamic model to assess the impact of TB treatment and vaccination strategies in Nigeria, focusing on the comparative analysis of untreated and treated populations, as well as evaluating mortality and recovery outcomes. Through simulations conducted using the Berkeley Madonna Software, it was observed that the populations of latent and susceptible individuals exhibit a near-equivalence, yet the cohort undergoing treatment markedly surpasses other groups. Interestingly, the infected demographic aligns closely with the average values across all compartments. An alarming trend was noted in chronic patients, whose numbers initially increase, followed by a decline over a six-year period, and then a subsequent rise, while the count of treated individuals demonstrates a continuous decrease. The study further reveals a pressing need for treatment among vaccinated individuals, highlighting a nuanced interplay between vaccination and therapeutic interventions. By employing stability and sensitivity analyses, this research underscores the critical importance of treatment in managing TB infection, advocating for enhanced strategies to mitigate the spread of this infectious disease. The findings contribute valuable insights into the dynamics of TB infection and the pivotal role of treatment, underscoring the necessity for integrated approaches in addressing the TB epidemic, particularly in regions burdened by high infection rates. 03-27-2024 <![CDATA[ <p>Tuberculosis (TB), an airborne disease caused by Mycobacterium, poses a significant global health challenge due to its rapid transmission through air and interaction with infected individuals. This study presents a comprehensive dynamic model to assess the impact of TB treatment and vaccination strategies in Nigeria, focusing on the comparative analysis of untreated and treated populations, as well as evaluating mortality and recovery outcomes. Through simulations conducted using the Berkeley Madonna Software, it was observed that the populations of latent and susceptible individuals exhibit a near-equivalence, yet the cohort undergoing treatment markedly surpasses other groups. Interestingly, the infected demographic aligns closely with the average values across all compartments. An alarming trend was noted in chronic patients, whose numbers initially increase, followed by a decline over a six-year period, and then a subsequent rise, while the count of treated individuals demonstrates a continuous decrease. The study further reveals a pressing need for treatment among vaccinated individuals, highlighting a nuanced interplay between vaccination and therapeutic interventions. By employing stability and sensitivity analyses, this research underscores the critical importance of treatment in managing TB infection, advocating for enhanced strategies to mitigate the spread of this infectious disease. The findings contribute valuable insights into the dynamics of TB infection and the pivotal role of treatment, underscoring the necessity for integrated approaches in addressing the TB epidemic, particularly in regions burdened by high infection rates.</p> ]]> Evaluating the Efficacy of Tuberculosis Management Strategies in Nigeria: A Mathematical Modelling Approach jafar anafi sharhabil tasiu doi: 10.56578/hf020105 Healthcraft Frontiers 03-27-2024 Healthcraft Frontiers 03-27-2024 2024 2 1 Article 46 10.56578/hf020105 https://www.acadlore.com/article/HF/2024_2_1/hf020105 https://www.acadlore.com/article/HF/2024_2_1/hf020104 Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost. 03-21-2024 <![CDATA[ <p>Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.</p> ]]> NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN xiaoxue hou ruibo liu youzhi zhang xuerong han jiachuan he he ma doi: 10.56578/hf020104 Healthcraft Frontiers 03-21-2024 Healthcraft Frontiers 03-21-2024 2024 2 1 Article 34 10.56578/hf020104 https://www.acadlore.com/article/HF/2024_2_1/hf020104 https://www.acadlore.com/article/HF/2024_2_1/hf020103 Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep learning is extensively applied in the realm of medicine and disease classification. Early diagnosis of pneumonia is essential so it can be efficiently treated with the type of antibiotics. Bacterium and viruses are the population's first cause of pneumonia and death. Bacteria and viruses are part of mammalian pathogens and the most invasive type of bacteria or virus causing many diseases. Bacterial infection is among the most common types of disease in all age groups, but most bacterial infectious diseases are not the same. Our research will propose a transfer learning-based approach for pneumonia prediction utilizing a dataset comprising chest X-ray images. The dataset-based images will be grouped into two groups based on the parameters. Our proposed model displayed an average accuracy of 94.54% on the dataset. The proposed model (PDTLA) performed well compared with previous quantitative and qualitative research studies. Pneumonia detection transfer learning algorithm (PDTLA) is the name of the modified model. 03-14-2024 <![CDATA[ Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep learning is extensively applied in the realm of medicine and disease classification. Early diagnosis of pneumonia is essential so it can be efficiently treated with the type of antibiotics. Bacterium and viruses are the population's first cause of pneumonia and death. Bacteria and viruses are part of mammalian pathogens and the most invasive type of bacteria or virus causing many diseases. Bacterial infection is among the most common types of disease in all age groups, but most bacterial infectious diseases are not the same. Our research will propose a transfer learning-based approach for pneumonia prediction utilizing a dataset comprising chest X-ray images. The dataset-based images will be grouped into two groups based on the parameters. Our proposed model displayed an average accuracy of 94.54% on the dataset. The proposed model (PDTLA) performed well compared with previous quantitative and qualitative research studies. Pneumonia detection transfer learning algorithm (PDTLA) is the name of the modified model. ]]> Pneumonia Detection Technique Empowered with Transfer Learning Approach muhammad daniyal baig hafiz burhan ul haq muhammad nauman irshad waseem akram doi: 10.56578/hf020103 Healthcraft Frontiers 03-14-2024 Healthcraft Frontiers 03-14-2024 2024 2 1 Article 20 10.56578/hf020103 https://www.acadlore.com/article/HF/2024_2_1/hf020103 https://www.acadlore.com/article/HF/2024_2_1/hf020102 In Banda Aceh City, Indonesia, particularly in Punge Jurong Gampong, the effectiveness of child oral health service interventions is notably impacted by the level of maternal knowledge and involvement. This quasi-experimental study was designed to scrutinize the impact of maternal behaviors on the maintenance of children's dental and oral health, employing a primary verbal healthcare strategy. Utilizing a pre-test and post-test approach, the research encompassed 45 mothers in the intervention group and an equal number in the control group. The intervention primarily consisted of educating mothers about the critical importance of dental and oral health, integrating promotional and preventive measures. The findings of this study reveal that maternal influence is a pivotal factor in shaping the oral health habits of children, with such influence being modulated by variables including cultural perceptions, socioeconomic status, educational background, and information accessibility. The range of maternal activities observed varied significantly, encompassing diligent teeth brushing practices and challenges in recognizing the significance of primary teeth. The study underlines a substantial need for customized, culturally sensitive interventions tailored to the unique context of Punge Jurong Gampong. It was observed that while the average knowledge level and Hypertext Preprocessor (PHP)-M scores of mothers in both the intervention and control groups did not show a significant difference, notable variances in attitudes and behaviors related to oral health were statistically significant (p&gt;0.05). These results highlight the criticality of context-specific, culturally informed educational programs in improving pediatric oral health outcomes. The study emphasizes the role of collaborative efforts involving healthcare professionals, community leaders, and educational institutions in creating an enabling environment for the effective implementation of primary oral healthcare strategies. Thus, this research contributes to the understanding of the multifaceted nature of maternal influence on child oral health and underscores the necessity of personalized and culturally adaptive educational interventions. 01-18-2024 <![CDATA[ In Banda Aceh City, Indonesia, particularly in Punge Jurong Gampong, the effectiveness of child oral health service interventions is notably impacted by the level of maternal knowledge and involvement. This quasi-experimental study was designed to scrutinize the impact of maternal behaviors on the maintenance of children's dental and oral health, employing a primary verbal healthcare strategy. Utilizing a pre-test and post-test approach, the research encompassed 45 mothers in the intervention group and an equal number in the control group. The intervention primarily consisted of educating mothers about the critical importance of dental and oral health, integrating promotional and preventive measures. The findings of this study reveal that maternal influence is a pivotal factor in shaping the oral health habits of children, with such influence being modulated by variables including cultural perceptions, socioeconomic status, educational background, and information accessibility. The range of maternal activities observed varied significantly, encompassing diligent teeth brushing practices and challenges in recognizing the significance of primary teeth. The study underlines a substantial need for customized, culturally sensitive interventions tailored to the unique context of Punge Jurong Gampong. It was observed that while the average knowledge level and Hypertext Preprocessor (PHP)-M scores of mothers in both the intervention and control groups did not show a significant difference, notable variances in attitudes and behaviors related to oral health were statistically significant (p&gt;0.05). These results highlight the criticality of context-specific, culturally informed educational programs in improving pediatric oral health outcomes. The study emphasizes the role of collaborative efforts involving healthcare professionals, community leaders, and educational institutions in creating an enabling environment for the effective implementation of primary oral healthcare strategies. Thus, this research contributes to the understanding of the multifaceted nature of maternal influence on child oral health and underscores the necessity of personalized and culturally adaptive educational interventions. ]]> Impact of Maternal Health Education on Pediatric Oral Health in Banda Aceh: A Quasi-Experimental Study reca reca cut aja nuraskin salikun salikun wahyu jati dyah utami linda suryani teuku salfiyadi mufizarni mufizarni eka sri rahayu ainun mardiah buchari buchari doi: 10.56578/hf020102 Healthcraft Frontiers 01-18-2024 Healthcraft Frontiers 01-18-2024 2024 2 1 Article 10 10.56578/hf020102 https://www.acadlore.com/article/HF/2024_2_1/hf020102 https://www.acadlore.com/article/HF/2024_2_1/hf020101 This study investigates the impact of nutritional status on academic performance among schoolchildren in Eastern Morocco. Focusing on the prevalence of overweight, obesity, and their associations with academic outcomes, the research underscores the significance of physical well-being in educational achievement. Conducted as a cross-sectional analysis in March 2022, the survey encompassed eight public and two private schools, selected through random sampling. Classes within these schools were also randomly chosen. Utilizing a self-administered, anonymous questionnaire, completed individually by students in the presence of a trained dietician, the study also involved anthropometric measurements and clinical examinations. Additionally, students' grade point averages (GPAs) were obtained from school records. The survey comprised 596 students, with an average age of 14.86 ± 1.98 years, height of 160.47 ± 11.84 cm, and weight of 51.28 ± 11.49 kg. The prevalence of underweight was recorded at 8.7%, overweight at 10.7%, and obesity at 2.7%. Statistical analysis using the Analysis of Variance (ANOVA) test revealed a significant association between obesity and diminished academic performance, indicating the need for attention to obesity among adolescents in this region. The findings suggest that national-level prevalence determination of overweight and obesity by health policymakers is crucial for this age group. Identifying risk factors associated with these conditions is imperative for effective prevention and early intervention. In this context, the promotion of physical activity and healthy eating habits is vital for fostering healthy, successful school environments. This research contributes to the understanding of how physical health, particularly nutritional status, influences academic outcomes. It highlights the need for integrated approaches that consider the physical well-being of students as a critical factor in educational success. The study's implications extend beyond academic circles, offering insights for policymakers and educators in developing holistic strategies to enhance both health and educational outcomes. 01-10-2024 <![CDATA[ <p>This study investigates the impact of nutritional status on academic performance among schoolchildren in Eastern Morocco. Focusing on the prevalence of overweight, obesity, and their associations with academic outcomes, the research underscores the significance of physical well-being in educational achievement. Conducted as a cross-sectional analysis in March 2022, the survey encompassed eight public and two private schools, selected through random sampling. Classes within these schools were also randomly chosen. Utilizing a self-administered, anonymous questionnaire, completed individually by students in the presence of a trained dietician, the study also involved anthropometric measurements and clinical examinations. Additionally, students' grade point averages (GPAs) were obtained from school records. The survey comprised 596 students, with an average age of 14.86 ± 1.98 years, height of 160.47 ± 11.84 cm, and weight of 51.28 ± 11.49 kg. The prevalence of underweight was recorded at 8.7%, overweight at 10.7%, and obesity at 2.7%. Statistical analysis using the Analysis of Variance (ANOVA) test revealed a significant association between obesity and diminished academic performance, indicating the need for attention to obesity among adolescents in this region. The findings suggest that national-level prevalence determination of overweight and obesity by health policymakers is crucial for this age group. Identifying risk factors associated with these conditions is imperative for effective prevention and early intervention. In this context, the promotion of physical activity and healthy eating habits is vital for fostering healthy, successful school environments. This research contributes to the understanding of how physical health, particularly nutritional status, influences academic outcomes. It highlights the need for integrated approaches that consider the physical well-being of students as a critical factor in educational success. The study's implications extend beyond academic circles, offering insights for policymakers and educators in developing holistic strategies to enhance both health and educational outcomes.</p> ]]> Influence of Nutritional Status on Academic Performance: A Study of Schoolchildren in Eastern Morocco said bouchefra rachid el chaal abdellatif bour doi: 10.56578/hf020101 Healthcraft Frontiers 01-10-2024 Healthcraft Frontiers 01-10-2024 2024 2 1 Article 1 10.56578/hf020101 https://www.acadlore.com/article/HF/2024_2_1/hf020101 https://www.acadlore.com/article/HF/2023_1_1/hf010105 The depth of a decision tree (DT) affects the performance of a DT classifier in predicting mortality caused by heart failure (HF). A deeper tree learns complex patterns within the data, theoretically leading to better predictive performance. A very deep tree also leads to overfitting, because the model learns the training data rather than generalize to new and unseen data, resulting in a lower classification performance on test data. Similarly, a shallow tree does not learn much of the complexity within the data, leading to underfitting and a lower performance. The pruning method has been proposed to set a limit on the maximum tree depth or the minimum number of instances required to split a node to reduce the computational complexity. Pruning helps avoid overfitting. However, it does not help find the optimal depth of the tree. To build a better-performing DT classifier, it is crucial to find the optimal tree depth to achieve optimal performance. This study proposed cross-validation to find the optimal tree depth using validation data. In the proposed method, the cross-validated accuracy for training and test data is empirically tested using the HF dataset, which contains 299 observations with 11 features collected from the Kaggle machine learning (ML) data repository. The observed result reveals that tuning the DT depth is significantly important to balance the learning process of the DT because relevant patterns are captured and overfitting is avoided. Although cross-validation techniques prove to be effective in determining the optimal DT depth, this study does not compare different methods to determine the optimal depth, such as grid search, pruning algorithms, or information criteria. This is the limitation of this study. 12-29-2023 <![CDATA[ The depth of a decision tree (DT) affects the performance of a DT classifier in predicting mortality caused by heart failure (HF). A deeper tree learns complex patterns within the data, theoretically leading to better predictive performance. A very deep tree also leads to overfitting, because the model learns the training data rather than generalize to new and unseen data, resulting in a lower classification performance on test data. Similarly, a shallow tree does not learn much of the complexity within the data, leading to underfitting and a lower performance. The pruning method has been proposed to set a limit on the maximum tree depth or the minimum number of instances required to split a node to reduce the computational complexity. Pruning helps avoid overfitting. However, it does not help find the optimal depth of the tree. To build a better-performing DT classifier, it is crucial to find the optimal tree depth to achieve optimal performance. This study proposed cross-validation to find the optimal tree depth using validation data. In the proposed method, the cross-validated accuracy for training and test data is empirically tested using the HF dataset, which contains 299 observations with 11 features collected from the Kaggle machine learning (ML) data repository. The observed result reveals that tuning the DT depth is significantly important to balance the learning process of the DT because relevant patterns are captured and overfitting is avoided. Although cross-validation techniques prove to be effective in determining the optimal DT depth, this study does not compare different methods to determine the optimal depth, such as grid search, pruning algorithms, or information criteria. This is the limitation of this study. ]]> Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality tsehay admassu assegie ahmed elaraby doi: 10.56578/hf010105 Healthcraft Frontiers 12-29-2023 Healthcraft Frontiers 12-29-2023 2023 1 1 Article 58 10.56578/hf010105 https://www.acadlore.com/article/HF/2023_1_1/hf010105 https://www.acadlore.com/article/HF/2023_1_1/hf010104 This study introduces an advanced forecasting method, utilizing a higher-order circular Pythagorean fuzzy time series (C-PyFTSs) approach, for the prediction of Alzheimer’s disease progression. Distinct from traditional forecasting methodologies, this novel approach is grounded in the principles of circular Pythagorean fuzzy set (C-PyFS) theory. It uniquely incorporates both positive and negative membership values, further augmented by a circular radius. This design is specifically tailored to address the inherent uncertainties and imprecisions prevalent in medical data. A key innovation of this method is its consideration of the circular nature of time series, which significantly enhances the accuracy and robustness of the forecasts. The higher-order aspect of this forecasting method facilitates a more comprehensive predictive model, surpassing the capabilities of existing techniques. The efficacy of this method has been rigorously evaluated through extensive experiments, benchmarked against conventional time series forecasting methods. The empirical results underscore the superiority of the proposed method in accurately predicting the trajectory of Alzheimer’s disease. This advancement holds substantial promise for improving prognostic assessments in clinical settings, offering a more nuanced understanding of disease progression. 12-21-2023 <![CDATA[ <p style="text-align: justify">This study introduces an advanced forecasting method, utilizing a higher-order circular Pythagorean fuzzy time series (C-PyFTSs) approach, for the prediction of Alzheimer’s disease progression. Distinct from traditional forecasting methodologies, this novel approach is grounded in the principles of circular Pythagorean fuzzy set (C-PyFS) theory. It uniquely incorporates both positive and negative membership values, further augmented by a circular radius. This design is specifically tailored to address the inherent uncertainties and imprecisions prevalent in medical data. A key innovation of this method is its consideration of the circular nature of time series, which significantly enhances the accuracy and robustness of the forecasts. The higher-order aspect of this forecasting method facilitates a more comprehensive predictive model, surpassing the capabilities of existing techniques. The efficacy of this method has been rigorously evaluated through extensive experiments, benchmarked against conventional time series forecasting methods. The empirical results underscore the superiority of the proposed method in accurately predicting the trajectory of Alzheimer’s disease. This advancement holds substantial promise for improving prognostic assessments in clinical settings, offering a more nuanced understanding of disease progression.</p> ]]> Enhanced Forecasting of Alzheimer’s Disease Progression Using Higher-Order Circular Pythagorean Fuzzy Time Series muhammad shakir chohan shahzaib ashraf keles dong doi: 10.56578/hf010104 Healthcraft Frontiers 12-21-2023 Healthcraft Frontiers 12-21-2023 2023 1 1 Article 44 10.56578/hf010104 https://www.acadlore.com/article/HF/2023_1_1/hf010104 https://www.acadlore.com/article/HF/2023_1_1/hf010103 Epilepsy, the most prevalent neurological disorder, is marked by spontaneous, recurrent seizures due to widespread neuronal discharges in the brain. This condition afflicts approximately 1% of the global population, with only two-thirds responding to antiepileptic drugs and a smaller fraction benefiting from surgical interventions. The social stigma and emotional distress associated with epilepsy underscore the importance of timely and accurate seizure detection, which can significantly enhance patient outcomes and quality of life. This research introduces a novel convolutional neural network (CNN) architecture for epileptic seizure detection, leveraging electroencephalogram (EEG) signals. Contrasted with traditional machine-learning methodologies, this innovative approach demonstrates superior performance in seizure prediction. The accuracy of the proposed CNN model is established at 97.52%, outperforming the highest accuracy of 93.65% achieved by the Discriminant Analysis classifier among the various classifiers evaluated. The findings of this study not only present a groundbreaking method in the realm of epileptic seizure recognition but also reinforce the potential of deep learning techniques in medical diagnostics. 11-30-2023 <![CDATA[ Epilepsy, the most prevalent neurological disorder, is marked by spontaneous, recurrent seizures due to widespread neuronal discharges in the brain. This condition afflicts approximately 1% of the global population, with only two-thirds responding to antiepileptic drugs and a smaller fraction benefiting from surgical interventions. The social stigma and emotional distress associated with epilepsy underscore the importance of timely and accurate seizure detection, which can significantly enhance patient outcomes and quality of life. This research introduces a novel convolutional neural network (CNN) architecture for epileptic seizure detection, leveraging electroencephalogram (EEG) signals. Contrasted with traditional machine-learning methodologies, this innovative approach demonstrates superior performance in seizure prediction. The accuracy of the proposed CNN model is established at 97.52%, outperforming the highest accuracy of 93.65% achieved by the Discriminant Analysis classifier among the various classifiers evaluated. The findings of this study not only present a groundbreaking method in the realm of epileptic seizure recognition but also reinforce the potential of deep learning techniques in medical diagnostics. ]]> A CNN Approach for Enhanced Epileptic Seizure Detection Through EEG Analysis nadide yucel hursit burak mutlu fatih durmaz emine cengil muhammed yildirim doi: 10.56578/hf010103 Healthcraft Frontiers 11-30-2023 Healthcraft Frontiers 11-30-2023 2023 1 1 Article 33 10.56578/hf010103 https://www.acadlore.com/article/HF/2023_1_1/hf010103 https://www.acadlore.com/article/HF/2023_1_1/hf010102 In the field of computer-aided diagnostics, the segmentation and classification of biomedical images play a pivotal role. This study introduces a novel approach employing a Self-Augmented Multistage Deep Learning Network (SAMNetwork) and Deep Belief Networks (DBNs) optimized by Coot Optimization Algorithms (COAs) for the analysis of dermoscopy images. The unique challenges posed by dermoscopy images, including complex detection backgrounds and lesion characteristics, necessitate advanced techniques for accurate lesion recognition. Traditional methods have predominantly focused on utilizing larger, more complex models to increase detection accuracy, yet have often neglected the significant intraclass variability and inter-class similarity of lesion traits. This oversight has led to challenges in algorithmic application to larger models. The current research addresses these limitations by leveraging SAM, which, although not yielding immediate high-quality segmentation for medical image data, provides valuable masks, features, and stability scores for developing and training enhanced medical images. Subsequently, DBNs, aided by COAs to fine-tune their hyper-parameters, perform the classification task. The effectiveness of this methodology was assessed through comprehensive experimental comparisons and feature visualization analyses. The results demonstrated the superiority of the proposed approach over the current state-of-the-art deep learning-based methods across three datasets: ISBI 2017, ISBI 2018, and the PH2 dataset. In the experimental evaluations, the Multi-class Dilated D-Net (MD2N) model achieved a Matthew’s Correlation Coefficient (MCC) of 0.86201, the Deep convolutional neural networks (DCNN) model 0.84111, the standalone DBN 0.91157, the autoencoder (AE) model 0.88662, and the DBN-COA model 0.93291, respectively. These findings highlight the enhanced performance and potential of integrating SAM with optimized DBNs in the detection and classification of skin cancer in dermoscopy images, marking a significant advancement in the field of medical image analysis. 11-30-2023 <![CDATA[ In the field of computer-aided diagnostics, the segmentation and classification of biomedical images play a pivotal role. This study introduces a novel approach employing a Self-Augmented Multistage Deep Learning Network (SAMNetwork) and Deep Belief Networks (DBNs) optimized by Coot Optimization Algorithms (COAs) for the analysis of dermoscopy images. The unique challenges posed by dermoscopy images, including complex detection backgrounds and lesion characteristics, necessitate advanced techniques for accurate lesion recognition. Traditional methods have predominantly focused on utilizing larger, more complex models to increase detection accuracy, yet have often neglected the significant intraclass variability and inter-class similarity of lesion traits. This oversight has led to challenges in algorithmic application to larger models. The current research addresses these limitations by leveraging SAM, which, although not yielding immediate high-quality segmentation for medical image data, provides valuable masks, features, and stability scores for developing and training enhanced medical images. Subsequently, DBNs, aided by COAs to fine-tune their hyper-parameters, perform the classification task. The effectiveness of this methodology was assessed through comprehensive experimental comparisons and feature visualization analyses. The results demonstrated the superiority of the proposed approach over the current state-of-the-art deep learning-based methods across three datasets: ISBI 2017, ISBI 2018, and the PH2 dataset. In the experimental evaluations, the Multi-class Dilated D-Net (MD2N) model achieved a Matthew’s Correlation Coefficient (MCC) of 0.86201, the Deep convolutional neural networks (DCNN) model 0.84111, the standalone DBN 0.91157, the autoencoder (AE) model 0.88662, and the DBN-COA model 0.93291, respectively. These findings highlight the enhanced performance and potential of integrating SAM with optimized DBNs in the detection and classification of skin cancer in dermoscopy images, marking a significant advancement in the field of medical image analysis. ]]> Segmentation and Classification of Skin Cancer in Dermoscopy Images Using SAM-Based Deep Belief Networks syed ziaur rahman tejesh reddy singasani khaja shareef shaik doi: 10.56578/hf010102 Healthcraft Frontiers 11-30-2023 Healthcraft Frontiers 11-30-2023 2023 1 1 Article 15 10.56578/hf010102 https://www.acadlore.com/article/HF/2023_1_1/hf010102 https://www.acadlore.com/article/HF/2023_1_1/hf010101 Recent years have seen a significant increase in the incidence of falls among the elderly, leading to accidental injuries and fatalities. This trend underscores the critical need for accurate fall risk assessment, a major concern for public health and safety. In addressing this challenge, a novel approach has been developed, leveraging a pressure sensor placed on the foot's sole to gather gait data from elderly individuals. This method provides a precise analysis of gait stability on a daily basis. The research introduced here utilizes the gramian angular summation field (GASF) technique for converting this data into two-dimensional images, which are then processed using an enhanced EfficientNet model. The innovation lies in the integration of a convolutional block attention module (CBAM) into this model, resulting in a CBAM-EfficientNet algorithm. This approach includes freezing the first four stages of the EfficientNet model, focusing training on the deeper layers that incorporate CBAM. This strategy is aimed at augmenting the network's ability to extract critical features from foot pressure data, consequently improving the accuracy of fall risk classification. Experimental evaluation of this optimized model demonstrates a classification accuracy of 98.5% and a sensitivity of 99.0%, indicating its practical efficacy and strong generalization capacity. These findings reveal that the methodology significantly enhances the classification of plantar pressure data, offering valuable support in medical diagnosis and has substantial practical implications. 11-30-2023 <![CDATA[ <p style="text-align: justify">Recent years have seen a significant increase in the incidence of falls among the elderly, leading to accidental injuries and fatalities. This trend underscores the critical need for accurate fall risk assessment, a major concern for public health and safety. In addressing this challenge, a novel approach has been developed, leveraging a pressure sensor placed on the foot's sole to gather gait data from elderly individuals. This method provides a precise analysis of gait stability on a daily basis. The research introduced here utilizes the gramian angular summation field (GASF) technique for converting this data into two-dimensional images, which are then processed using an enhanced EfficientNet model. The innovation lies in the integration of a convolutional block attention module (CBAM) into this model, resulting in a CBAM-EfficientNet algorithm. This approach includes freezing the first four stages of the EfficientNet model, focusing training on the deeper layers that incorporate CBAM. This strategy is aimed at augmenting the network's ability to extract critical features from foot pressure data, consequently improving the accuracy of fall risk classification. Experimental evaluation of this optimized model demonstrates a classification accuracy of 98.5% and a sensitivity of 99.0%, indicating its practical efficacy and strong generalization capacity. These findings reveal that the methodology significantly enhances the classification of plantar pressure data, offering valuable support in medical diagnosis and has substantial practical implications.</p> ]]> Enhancing Fall Risk Assessment in the Elderly: A Study Utilizing Transfer Learning in an Improved EfficientNet Network with the Gramian Angular Field Technique congcong li yueting cai laith jaafer habeeb atta-ur rahman ritzkal doi: 10.56578/hf010101 Healthcraft Frontiers 11-30-2023 Healthcraft Frontiers 11-30-2023 2023 1 1 Article 1 10.56578/hf010101 https://www.acadlore.com/article/HF/2023_1_1/hf010101