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Special Issue:
Advanced Computational Techniques in Healthcare: Applications in Disease Detection, Assessment, and Forecasting
he ma
Lead Guest Editor
Northeastern University, China
Research interests: Medical Artificial Intelligence; Medical Image Processing
fatih özyurt
Guest Editor
Firat University, Turkey
Research interests: Pattern Recognition; Artificial Neural Networks; Image Processing; Deep Learning; Information Security
yan pei
Guest Editor
University of Aizu, Japan
Research interests: Computational Intelligence; Interactive Evolutionary Computation; Machine Learning

Details of the Special Issue

Special Issue: Advanced Computational Techniques in Healthcare: Applications in Disease Detection, Assessment, and Forecasting

The rapid convergence of healthcare and advanced computational technology marks a pivotal chapter in medical science and patient care. The healthcare sector, facing the dual challenges of an aging global population and escalating healthcare costs, is in dire need of innovative solutions for disease management, patient care, and healthcare delivery. The advent of powerful computational tools and techniques, such as machine learning, deep learning, artificial intelligence, and big data analytics, is reshaping the landscape of medical diagnostics, treatment personalization, and healthcare administration.

These technological advancements are not just augmenting the existing healthcare infrastructure but are also paving the way for new paradigms in medical research and patient care. For instance, predictive analytics and AI algorithms are enabling early detection of diseases, which is crucial for effective treatment. Personalized medicine, powered by genomic data analysis and AI, is tailoring treatments to individual patient needs, thereby improving treatment outcomes. Moreover, the integration of IoT and wearable technologies is revolutionizing patient monitoring, facilitating real-time health data collection, and enabling remote care – a significant step forward in managing chronic diseases and improving overall patient quality of life.

This special issue, "Advanced Computational Techniques in Healthcare: Applications in Disease Detection, Assessment, and Forecasting," seeks to capture the essence of this technological revolution in healthcare. We aim to highlight research that not only addresses the challenges in healthcare but also showcases the potential of computational technologies as a cornerstone for future advancements in medical science. The special issue will feature a curated collection of research articles, reviews, and case studies that demonstrate the innovative application of computational techniques in various aspects of healthcare.

Invited Topics:

The topics of interest for this special issue include, but are not limited to:

  • Innovative Machine Learning Applications in Healthcare: Exploration of novel machine learning models and algorithms in various healthcare applications, including disease prediction, diagnosis, and treatment personalization.

  • Deep Learning for Medical Imaging and Diagnostics: Use of deep learning techniques in analyzing medical images, including MRI, CT scans, X-rays, and ultrasound, for improved diagnosis and patient care.

  • AI-driven Predictive Analytics for Disease Management: Implementation of artificial intelligence in predicting disease outbreaks, patient health deterioration, and healthcare needs forecasting.

  • Big Data Analytics in Healthcare: Leveraging big data for enhanced decision-making in healthcare management, policy making, and patient care strategies.

  • Computational Genomics and Personalized Medicine: Application of computational methods in genomics for personalized treatment plans and understanding genetic factors in diseases.

  • Wearable Technology and IoT in Patient Monitoring: Use of wearable devices and IoT for continuous health monitoring, remote patient care, and chronic disease management.

  • Natural Language Processing in Healthcare Data Analysis: Utilizing NLP for extracting valuable insights from healthcare records, patient feedback, and medical literature.

  • Blockchain Technology for Secure Healthcare Data Management: Exploring blockchain applications for ensuring data security, patient privacy, and efficient healthcare data management.

  • Telemedicine and Digital Health Innovations: Advances in telehealth technologies, digital therapeutics, and virtual healthcare delivery systems.

  • Computational Techniques in Public Health and Epidemiology: Utilizing computational methods in tracking disease spread, public health data analysis, and epidemiological studies.

  • Ethical, Legal, and Social Implications of AI in Healthcare: Addressing the ethical and legal challenges posed by the implementation of AI and computational technologies in healthcare settings.

  • Case Studies on Computational Technology Implementation in Healthcare: Real-world examples and case studies demonstrating the practical application and impact of computational technologies in healthcare facilities.

  • Integration of AI in Clinical Decision Support Systems: Development and application of AI-based tools and systems to aid clinicians in making informed decisions.

  • Challenges and Opportunities in Digital Health Transformation: Discussion on the challenges faced during the digital transformation of healthcare and the opportunities it presents.

Submission Details:

We invite contributions from researchers, academicians, and industry professionals that reflect the latest advancements and practical applications of computational technology in healthcare. Submissions should be original, technically sound, and provide meaningful insights into addressing healthcare challenges. Authors should also discuss the broader impact of their work on healthcare systems, policy, and future research directions.

For submission guidelines, formatting requirements, and deadlines, please visit https://www.acadlore.com/journals/HF. All submissions must be sent to hf@acadlore.com, indicating that they are for this special issue.

Published Articles

Abstract

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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.

Open Access
Research article
Special Issue
Segmentation and Classification of Skin Cancer in Dermoscopy Images Using SAM-Based Deep Belief Networks
syed ziaur rahman ,
tejesh reddy singasani ,
khaja shareef shaik
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Available online: 11-30-2023

Abstract

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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.
Open Access
Research article
Special Issue
A CNN Approach for Enhanced Epileptic Seizure Detection Through EEG Analysis
nadide yucel ,
hursit burak mutlu ,
fatih durmaz ,
emine cengil ,
muhammed yildirim
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Available online: 11-30-2023

Abstract

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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.
Open Access
Research article
Special Issue
Enhanced Forecasting of Alzheimer’s Disease Progression Using Higher-Order Circular Pythagorean Fuzzy Time Series
muhammad shakir chohan ,
shahzaib ashraf ,
keles dong
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Available online: 12-21-2023

Abstract

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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.

Open Access
Research article
Special Issue
Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality
tsehay admassu assegie ,
ahmed elaraby
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Available online: 12-29-2023

Abstract

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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.
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