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Computers
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Machine and Deep Learning in the Health Domain 2024
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Machine and Deep Learning in the Health Domain 2024

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 5374

Special Issue Editor

Dr. Hersh Sagreiya Sagreiya

E-Mail Website
Guest Editor
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: machine learning; deep learning; informatics; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a recent revolution in the application of machine learning and deep learning within healthcare, with interest in this area increasing exponentially at both medical society meetings and computer science conferences. Unlike prior attempts at medical AI and computer-aided diagnosis, these algorithms do not rely on predetermined features and can discern patterns in the data that would be impossible for an individual to detect.

The healthcare domain provides rich data that these algorithms can draw upon, including clinical notes, vital signs, laboratory values, genomic data, pathology, radiological images, and medical sensors, just to name a few. In addition, multi-modal and omics data may be applied to solve clinical problems. These data can be used to achieve multiple goals, including diagnosing diseases, prognosticating clinical outcomes, determining responses to therapy, patient monitoring, and drug as well as device development. In addition, these technologies provide researchers with the opportunity to enhance their understanding of disease pathogenesis, leveraging both large volumes of data and advanced machine learning techniques.

These developments allow for new frontiers in medicine. These include learning healthcare systems that improve with time as they incorporate increasing volumes of multimodal data from diverse patient populations. They also enable personalized medicine, the tailoring of healthcare to individual patients. Meanwhile, it is crucial that these algorithms remain robust to perturbations in the input data while remaining trustworthy, ethical, and free of bias. These techniques need to generalize well to heterogeneous patient populations, while maintaining and ultimately improving their performance in the populations in which they were developed. This Special Issue welcomes both original research articles and review articles that investigate the state of the art in machine learning and deep learning applied to healthcare. 

Dr. Hersh Sagreiya Sagreiya
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • medicine
  • health
  • disease diagnosis
  • disease prognostication
  • treatment effectiveness
  • electronic medical records
  • medical informatics

Published Papers (6 papers)

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15 pages, 606 KiB  
Article
Personalized Classifier Selection for EEG-Based BCIs
by Javad Rahimipour Anaraki, Antonina Kolokolova and Tom Chau
Computers 2024, 13(7), 158; https://doi.org/10.3390/computers13070158 - 21 Jun 2024
Viewed by 319
Abstract
The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and [...] Read more.
The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and inter-subject variability in EEG data, complicating the choice of the best classifier for different individuals over time. There is a keen need for an automatic approach to selecting a personalized classifier suited to an individual’s current needs. To this end, we have developed a systematic methodology for individual classifier selection, wherein the structural characteristics of an EEG dataset are used to predict a classifier that will perform with high accuracy. The method was evaluated using motor imagery EEG data from Physionet. We confirmed that our approach could consistently predict a classifier whose performance was no worse than the single-best-performing classifier across the participants. Furthermore, Kullback–Leibler divergences between reference distributions and signal amplitude and class label distributions emerged as the most important characteristics for classifier prediction, suggesting that classifier choice depends heavily on the morphology of signal amplitude densities and the degree of class imbalance in an EEG dataset. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
24 pages, 949 KiB  
Article
Advancing Skin Cancer Prediction Using Ensemble Models
by Priya Natha and Pothuraju RajaRajeswari
Computers 2024, 13(7), 157; https://doi.org/10.3390/computers13070157 - 21 Jun 2024
Viewed by 271
Abstract
There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other [...] Read more.
There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
17 pages, 3025 KiB  
Article
An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection
by Ayad E. Korial, Ivan Isho Gorial and Amjad J. Humaidi
Computers 2024, 13(6), 126; https://doi.org/10.3390/computers13060126 - 22 May 2024
Viewed by 564
Abstract
Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML [...] Read more.
Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
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17 pages, 4179 KiB  
Article
A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals
by Michelle Annice Tjitra, Nagisa Eremia Anju, Dodi Sudiana and Mia Rizkinia
Computers 2024, 13(5), 125; https://doi.org/10.3390/computers13050125 - 17 May 2024
Viewed by 691
Abstract
Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus [...] Read more.
Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
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25 pages, 2999 KiB  
Article
GFLASSO-LR: Logistic Regression with Generalized Fused LASSO for Gene Selection in High-Dimensional Cancer Classification
by Ahmed Bir-Jmel, Sidi Mohamed Douiri, Souad El Bernoussi, Ayyad Maafiri, Yassine Himeur, Shadi Atalla, Wathiq Mansoor and Hussain Al-Ahmad
Computers 2024, 13(4), 93; https://doi.org/10.3390/computers13040093 - 6 Apr 2024
Viewed by 1481
Abstract
Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable [...] Read more.
Advancements in genomic technologies have paved the way for significant breakthroughs in cancer diagnostics, with DNA microarray technology standing at the forefront of identifying genetic expressions associated with various cancer types. Despite its potential, the vast dimensionality of microarray data presents a formidable challenge, necessitating efficient dimension reduction and gene selection methods to accurately identify cancerous tumors. In response to this challenge, this study introduces an innovative strategy for microarray data dimension reduction and crucial gene set selection, aiming to enhance the accuracy of cancerous tumor identification. Leveraging DNA microarray technology, our method focuses on pinpointing significant genes implicated in tumor development, aiding the development of sophisticated computerized diagnostic tools. Our technique synergizes gene selection with classifier training within a logistic regression framework, utilizing a generalized Fused LASSO (GFLASSO-LR) regularizer. This regularization incorporates two penalties: one for selecting pertinent genes and another for emphasizing adjacent genes of importance to the target class, thus achieving an optimal trade-off between gene relevance and redundancy. The optimization challenge posed by our approach is tackled using a sub-gradient algorithm, designed to meet specific convergence prerequisites. We establish that our algorithm’s objective function is convex, Lipschitz continuous, and possesses a global minimum, ensuring reliability in the gene selection process. A numerical evaluation of the method’s parameters further substantiates its effectiveness. Experimental outcomes affirm the GFLASSO-LR methodology’s high efficiency in processing high-dimensional microarray data for cancer classification. It effectively identifies compact gene subsets, significantly enhancing classification performance and demonstrating its potential as a powerful tool in cancer research and diagnostics. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
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21 pages, 6859 KiB  
Systematic Review
A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions
by Yin Wang, Weibin Cheng, Fahim Sufi, Qiang Fang and Seedahmed S. Mahmoud
Computers 2024, 13(5), 117; https://doi.org/10.3390/computers13050117 - 9 May 2024
Viewed by 1085
Abstract
In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced [...] Read more.
In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 relevant documents, unveiling a landscape marked by significant advancements and persistent challenges. Through a methodological lens grounded in the PRISMA framework (Version 2020) and Machine Learning-driven tools like VosViewer (Version 1.6.20) and Litmaps (Free Version), the research delineates the high variability in speech patterns, the intricacies of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Innovative solutions such as specialized deep learning models, data augmentation strategies, and the pivotal role of interdisciplinary collaboration in dataset annotation emerge as vital contributions to this field. The analysis culminates in identifying theoretical and practical pathways for surmounting these barriers, highlighting the potential of deep learning technologies to revolutionize aphasia assessment and treatment. This review not only consolidates current knowledge but also charts a course for future research, emphasizing the need for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work underscores the transformative power of deep learning in advancing aphasia diagnosis, treatment, and support, heralding a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain 2024)
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