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Machine and Deep Learning in the Health Domain (3rd Edition)
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Machine and Deep Learning in the Health Domain (3rd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 4498

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 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 impossible for an individual to detect.

The healthcare domain provides rich data for these algorithms, 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 for diagnosing diseases, prognosticating clinical outcomes, determining responses to therapy, patient monitoring, and drug and device development. In addition, these technologies provide researchers with the opportunity to enhance their understanding of disease pathogenesis, leveraging large volumes of data and advanced machine learning techniques.

These developments allow for new medical frontiers. 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, tailoring 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 compared to the populations in which they were developed. This third edition of the 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 www.mdpi.com 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 250 words) can be sent to the Editorial Office for assessment.

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

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Related Special Issues

Published Papers (5 papers)

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Research

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14 pages, 1377 KB  
Article
Multi-Centre Liver Tumour Classification via Federated Learning: Investigating Data Heterogeneity, Transfer Learning, and Model Efficiency
by Degang Zhu, Shiqi Wei and Xinming Zhang
Computers 2026, 15(5), 286; https://doi.org/10.3390/computers15050286 - 1 May 2026
Viewed by 181
Abstract
This paper investigates federated multi-centre liver tumour classification from contrast-enhanced CT under realistic data heterogeneity and domain shift. To address the practical constraint that medical data are often siloed across institutions, we develop a FedProx-based federated learning pipeline that enables collaborative training without [...] Read more.
This paper investigates federated multi-centre liver tumour classification from contrast-enhanced CT under realistic data heterogeneity and domain shift. To address the practical constraint that medical data are often siloed across institutions, we develop a FedProx-based federated learning pipeline that enables collaborative training without exchanging raw patient data. Using the LiTS dataset as the training domain, we construct a slice-level binary classification task based on voxel-level annotations, while rigorously assessing out-of-distribution generalisation on an external held-out dataset, 3D-IRCADb. We conduct comprehensive experiments across multiple backbone architectures, including ResNet-50, EfficientNet-B3, ViT-B/16, and MobileNetV3-Small, comparing FedProx and FedAvg under three heterogeneity intensities (IID, mild non-IID, and severe non-IID). Furthermore, we evaluate transfer learning strategies, ranging from frozen backbones to partial fine-tuning of the last stage, and perform ablations on the proximal coefficient μ and local epochs E to characterise optimisation behaviour. Our results show that FedProx is generally comparable to FedAvg, with slightly more stable behaviour in some heterogeneous settings. We also observe a clear validation-to-external gap, indicating that external-domain robustness remains challenging and requires cautious interpretation for deployment. ImageNet pretraining yields consistent gains, particularly for data-sparse clients, while partial fine-tuning enhances adaptation to CT-specific features. Finally, MobileNetV3-Small offers a favourable performance–efficiency trade-off by reducing communication payload and computation cost, supporting practical deployment on resource-constrained clinical edge devices. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain (3rd Edition))
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17 pages, 1091 KB  
Article
ASD Recognition Through Weighted Integration of Landmark-Based Handcrafted and Pixel-Based Deep Learning Features
by Asahi Sekine, Abu Saleh Musa Miah, Koki Hirooka, Najmul Hassan, Md. Al Mehedi Hasan, Yuichi Okuyama, Yoichi Tomioka and Jungpil Shin
Computers 2026, 15(2), 124; https://doi.org/10.3390/computers15020124 - 13 Feb 2026
Viewed by 910
Abstract
Autism Spectrum Disorder (ASD) is a neurological condition that affects communication and social interaction skills, with individuals experiencing a range of challenges that often require specialized care. Automated systems for recognizing ASD face significant challenges due to the complexity of identifying distinguishing features [...] Read more.
Autism Spectrum Disorder (ASD) is a neurological condition that affects communication and social interaction skills, with individuals experiencing a range of challenges that often require specialized care. Automated systems for recognizing ASD face significant challenges due to the complexity of identifying distinguishing features from facial images. This study proposes an incremental advancement in ASD recognition by introducing a dual-stream model that combines handcrafted facial-landmark features with deep learning-based pixel-level features. The model processes images through two distinct streams to capture complementary aspects of facial information. In the first stream, facial landmarks are extracted using MediaPipe (v0.10.21),with a focus on 137 symmetric landmarks. The face’s position is adjusted using in-plane rotation based on eye-corner angles, and geometric features along with 52 blendshape features are processed through Dense layers. In the second stream, RGB image features are extracted using pre-trained CNNs (e.g., ResNet50V2, DenseNet121, InceptionV3) enhanced with Squeeze-and-Excitation (SE) blocks, followed by feature refinement through Global Average Pooling (GAP) and DenseNet layers. The outputs from both streams are fused using weighted concatenation through a softmax gate, followed by further feature refinement for classification. This hybrid approach significantly improves the ability to distinguish between ASD and non-ASD faces, demonstrating the benefits of combining geometric and pixel-based features. The model achieved an accuracy of 96.43% on the Kaggle dataset and 97.83% on the YTUIA dataset. Statistical hypothesis testing further confirms that the proposed approach provides a statistically meaningful advantage over strong baselines, particularly in terms of classification correctness and robustness across datasets. While these results are promising, they show incremental improvements over existing methods, and future work will focus on optimizing performance to exceed current benchmarks. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain (3rd Edition))
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19 pages, 2052 KB  
Article
Advanced Machine Learning Techniques for Predicting Inpatient Deterioration in General Medicine
by Said Al Jaadi, Laila Al Wahaibi, Mohammed Al-Hinai, Haneen Hafiz Gaffar and Abdullah M. Al Alawi
Computers 2026, 15(2), 123; https://doi.org/10.3390/computers15020123 - 12 Feb 2026
Viewed by 795
Abstract
Inpatient deterioration, marked by ICU transfer or mortality, remains a critical challenge in hospital settings. While traditional early warning systems (EWS) have limitations, machine learning (ML) offers a promising approach for the early identification of at-risk patients. This study aimed to develop and [...] Read more.
Inpatient deterioration, marked by ICU transfer or mortality, remains a critical challenge in hospital settings. While traditional early warning systems (EWS) have limitations, machine learning (ML) offers a promising approach for the early identification of at-risk patients. This study aimed to develop and validate multiple ML models for predicting inpatient deterioration among general medical patients using electronic health record (EHR) data. A retrospective cohort study was conducted on 524 patients admitted between January 2022 and December 2023. The dataset included demographic, clinical, and laboratory variables, with time-stamped measurements treated as distinct features. After excluding variables with >15% missing data, standard imputation was performed. The training data was balanced using the Synthetic Minority Over-sampling Technique (SMOTE), and feature selection was performed using SelectKBest. A range of models—including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines (SVMs), and Neural Networks—were trained and evaluated using AUC, accuracy, precision, recall, and F1-score. During 5-fold cross-validation, the models demonstrated high stability, with the Random Forest achieving a mean AUC of 0.980. On the final independent test set, the optimized Random Forest model yielded the highest performance with an AUC of 0.837 and an accuracy of 85.4%. Functional status, oxygen requirements, and urea levels were identified as key predictors. ML models, particularly Random Forest, can significantly enhance the early detection of inpatient deterioration. The contribution of this work lies in its systematic comparison of multiple algorithms and its robust methodology. Future research should focus on external validation, the integration of temporal data using recurrent neural network architectures, and the application of Explainable AI (XAI) to foster clinical trust and facilitate implementation. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain (3rd Edition))
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29 pages, 1055 KB  
Article
An Interpretable Multi-Dataset Learning Framework for Breast Cancer Prediction Using Clinical and Biomedical Tabular Data
by Muhammad Ateeb Ather, Abdullah, Zulaikha Fatima, José Luis Oropeza Rodríguez and Grigori Sidorov
Computers 2026, 15(2), 97; https://doi.org/10.3390/computers15020097 - 2 Feb 2026
Cited by 1 | Viewed by 966
Abstract
Despite the numerous advancements that have been made in the treatment and management of breast cancer, it continues to be a source of mortality in millions of female patients across the world each year; thus, there is a need for proper and reliable [...] Read more.
Despite the numerous advancements that have been made in the treatment and management of breast cancer, it continues to be a source of mortality in millions of female patients across the world each year; thus, there is a need for proper and reliable diagnostic assistance tools that are quite effective in the prediction of the disease in its early stages. In our research, in addition to the proposed framework, a comprehensive comparative assessment of traditional machine learning, deep learning, and transformer-based models has been performed to predict breast cancer in a multi-dataset environment. For the purpose of improving diversity and reducing any possible biases in the datasets, our research combined three datasets: breast cancer biopsy morphological (WDBC), biochemical and metabolic properties (Coimbra), and cytological attributes (WBCO), intended to expose the model to heterogeneous feature domains and evaluate robustness under distributional variation. Based on the thorough process conducted in our research involving traditional machine learning models, deep learning models, and transformers, a proposed hybrid architecture referred to as the FT-Transformer-Attention-LSTM-SVM framework has been designed and developed in our research that is compatible and well-suited for the processing and analysis of the given tabular biomedical datasets. The proposed design in the research has an effective performance of 99.90% accuracy in the primary test environment, an average mean accuracy of 99.56% in the 10-fold cross-validation process, and an accuracy of 98.50% in the WBCO test environment, with a considerable margin of significance less than 0.0001 in the paired two-sample t-test comparison process. In our research, we have performed the importance assessment in conjunction with the SHAP and LIME techniques and have demonstrated that its decisions are based upon important attributes such as the values of the attributes of radius, concavity, perimeter, compactness, and texture. Additionally, the research has conducted the ablation test and has proved the importance of the designed FT-Transformer. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain (3rd Edition))
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Review

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27 pages, 1825 KB  
Review
A Comparative Review of Quantum Neural Networks and Classical Machine Learning for Cardiovascular Disease Risk Prediction
by Nouf Ali AL Ajmi and Muhammad Shoaib
Computers 2026, 15(2), 102; https://doi.org/10.3390/computers15020102 - 2 Feb 2026
Viewed by 1196
Abstract
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making [...] Read more.
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making and improve early detection of cardiac risks. Although classical machine learning techniques have demonstrated strong performance in cardiovascular disease prediction, their efficiency and scalability are increasingly challenged by high-dimensional and large-scale medical datasets. Emerging advances in quantum computing have introduced quantum machine learning (QML) as a promising alternative, offering novel computational paradigms with the potential to outperform classical methods in terms of speed and problem-solving capability. This review analyzed twelve studies, evaluating data types, quantum architecture, performance metrics, and comparative efficacy against classical machine learning models. Our findings indicate that QNNs show promise for enhanced predictive accuracy and computational efficiency. However, significant challenges in scalability, noise resilience, and clinical integration persist. The translation of quantum advantage into clinical practice necessitates further validation on large-scale with diverse datasets. Full article
(This article belongs to the Special Issue Machine and Deep Learning in the Health Domain (3rd Edition))
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