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Healthcare
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Machine Learning in Medicine and Health: Data, Methods and Applications
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Machine Learning in Medicine and Health: Data, Methods and Applications

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 9797

Special Issue Editors

Dr. Grażyna Suchacka

E-Mail Website
Guest Editor
Institute of Informatics, Faculty of Mathematics, Physics and Computer Science, University of Opole, Pl. Kopernika 11a, 45-040 Opole, Poland
Interests: data analysis; machine learning; data mining; web usage mining
Special Issues, Collections and Topics in MDPI journals
Dr. Ioana Boeraş

E-Mail Website
Guest Editor Assistant
Biology and Ecology Research Center, Faculty of Sciences, Lucian Blaga University of Sibiu, 550012 Sibiu, Romania
Interests: microbiome; genomics; RNA; river ecology
Dr. Ionela Maniu

E-Mail Website
Guest Editor Assistant
1. Research Center in Informatics and Information Technology, Mathematics and Informatics Department, Faculty of Sciences, Lucian Blaga University, 550025 Sibiu, Romania
2. Pediatric Research Team, Clinical Pediatric Hospital, 550166 Sibiu, Romania
Interests: data mining; data analysis; machine learning; network analysis; data visualization; sustainable development; entrepreneurship; bibliometrics

Special Issue Information

Dear Colleagues,

Technological advances in different domains, including medicine, have generated vast and variate/complex healthcare data: demographics and clinical data; diagnoses, procedures, and medication-related information; laboratory tests; medical images data; omics-data; IoT/mHealth/app/sensor data; social media data; free text; speech data, etc. Handling these multidimensional structured/semi/unstructured data is challenging and requires the joint effort of specialists with different backgrounds (biology, physics, computer science, mathematics, engineering, etc.). Artificial intelligence and machine learning methods and tools can assist practitioners in converting these data into knowledge in order to improve health quality.

This Special Issue aims to present research on the application of supervised/unsupervised/semi-supervised machine learning approaches in different areas of the medicine and health sciences sectors, giving insights into the following: data collection, organization, and preprocessing; research design and study hypothesis; machine learning analysis methods, application, validation, performance, and interpretation; and the complexity, advantages, limitations, and challenges of application in practice. These resources could provide support for physicians/health care professionals, researchers, and students about how these data-driven methods could be applied to clinical practice and/or further research.

This Special Issue invites original research and review articles on the applications of machine learning in different areas of the medicine and health sciences to present researchers’ personal experiences with a wide array of machine learning methods.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in Applied Sciences.

Dr. Grażyna Suchacka
Guest Editor

Dr. Ioana Boeraş
Dr. Ionela Maniu
Guest Editor Assistants

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. Healthcare is an international peer-reviewed open access semimonthly 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 2700 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
  • laboratory (bio)markers
  • risk factors/risk scores
  • classification
  • prediction
  • (disease) profiling
  • diagnosis
  • prognosis
  • artificial intelligence
  • medicine
  • bioinformatics
  • bibliometric

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

17 pages, 1303 KB  
Article
Prediction of Adherence to an Online Wellness Program for People with Mobility Limitations: A Machine Learning Approach
by Salma Aly, Hui-Ju Young, James H. Rimmer and Tapan Mehta
Healthcare 2026, 14(6), 781; https://doi.org/10.3390/healthcare14060781 - 19 Mar 2026
Viewed by 406
Abstract
Background/Objectives: People with mobility limitations face disproportionately high rates of chronic health conditions and demonstrate lower adherence to wellness interventions. Digital programs such as MENTOR offer accessible alternatives but often face high rates of attrition. This study applied machine learning (ML) methods to [...] Read more.
Background/Objectives: People with mobility limitations face disproportionately high rates of chronic health conditions and demonstrate lower adherence to wellness interventions. Digital programs such as MENTOR offer accessible alternatives but often face high rates of attrition. This study applied machine learning (ML) methods to predict adherence to the eight-week MENTOR telewellness program and identify key predictors of participant attendance. Methods: Data were drawn from 1218 adults enrolled in MENTOR (2023–2024). Adherence was defined as the percentage of 40 sessions attended. Baseline demographic, socioeconomic, psychosocial, mindfulness, resilience, health status, and physical activity variables were included as predictors. Following preprocessing and imputation, 13 ML regression models were trained using an 80/20 train–test split. The best-performing model was identified using mean absolute error (MAE), followed by feature selection and SHAP interpretability analyses. Pairwise synergy analysis quantified interactions between top predictors. Results: Model performance was modest overall. Bayesian ridge regression achieved the best performance (MAE 20.98; RMSE 25.26; R2 = 0.12). SHAP analyses revealed that education, race, emotional support, Area Deprivation Index, household size, mindfulness, life satisfaction, and disability onset were the strongest predictors of adherence. Higher emotional support, mindfulness, and life satisfaction were associated with greater adherence, while socioeconomic disadvantage predicted lower adherence. Synergy analyses showed the strongest predictive interactions between low education and psychosocial resources (emotional support and life satisfaction). Conclusions: Baseline characteristics alone modestly predicted adherence to a digital wellness program. However, psychosocial and socioeconomic factors emerged as meaningful predictors, underscoring the need for personalized support strategies to reduce dropout among participants with mobility limitations. Full article
(This article belongs to the Special Issue Machine Learning in Medicine and Health: Data, Methods and Applications)
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39 pages, 5411 KB  
Article
Proof-of-Concept Machine Learning Framework for Arboviral Disease Classification Using Literature-Derived Synthetic Data: Methodological Development Preceding Clinical Validation
by Elí Cruz-Parada, Guillermina Vivar-Estudillo, Laura Pérez-Campos Mayoral, María Teresa Hernández-Huerta, Alma Dolores Pérez-Santiago, Carlos Romero-Diaz, Eduardo Pérez-Campos Mayoral, Iván A. García Montalvo, Lucia Martínez-Martínez, Héctor Martínez-Ruiz, Idarh Matadamas, Miriam Emily Avendaño-Villegas, Margarito Martínez Cruz, Hector Alejandro Cabrera-Fuentes, Aldo-Eleazar Pérez-Ramos, Eduardo Lorenzo Pérez-Campos and Carlos Mauricio Lastre-Domínguez
Healthcare 2026, 14(2), 247; https://doi.org/10.3390/healthcare14020247 - 19 Jan 2026
Cited by 1 | Viewed by 993
Abstract
Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world [...] Read more.
Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise. Full article
(This article belongs to the Special Issue Machine Learning in Medicine and Health: Data, Methods and Applications)
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19 pages, 590 KB  
Article
Utilization Patterns and Implementation Barriers in Adoption of Teledentistry Within Romanian Dental Practice
by Andrei Andronic, George Maniu, Victoria Birlutiu and Maria Popa
Healthcare 2025, 13(23), 3176; https://doi.org/10.3390/healthcare13233176 - 4 Dec 2025
Cited by 1 | Viewed by 810
Abstract
Background: Teledentistry constitutes a key component of digital health, enabling remote oral healthcare delivery through information and communication technologies (ICT). The COVID-19 pandemic accelerated its global adoption; however, data regarding its implementation within Romanian dental practice remain limited. Understanding usage patterns, perceived benefits, [...] Read more.
Background: Teledentistry constitutes a key component of digital health, enabling remote oral healthcare delivery through information and communication technologies (ICT). The COVID-19 pandemic accelerated its global adoption; however, data regarding its implementation within Romanian dental practice remain limited. Understanding usage patterns, perceived benefits, and implementation barriers is essential for effective integration. Objectives: This study examined the adoption of teledentistry among dental practitioners in Sibiu County, Romania, identified its main applications, assessed professional perceptions, and explored barriers and their interrelations using association rule mining (ARM). Methods: A cross-sectional online survey was distributed in 2025 to all 630 registered dentists in Sibiu County. The questionnaire collected demographic data, usage patterns, perceived benefits, and barriers. A total of 197 valid responses were obtained (response rate: 31.2%). Descriptive statistics, Chi-square tests, and ARM were used to identify associations between usage contexts and recorded obstacles. Results: Overall, 44.6% of respondents reported using teledentistry tools, primarily for interdisciplinary consultations (29.4%), postoperative counseling (26.4%), and treatment monitoring (25.3%). The most frequently cited barriers were the inability to perform direct clinical examinations (71.5%), practitioner reluctance (37.1%), insufficient infrastructure (29.9%), and the lack of a clear legislative framework (27.4%). ARM revealed frequent co-occurrence patterns among these barriers. Practitioners with prior experience in teledentistry reported significantly higher perceived utility (58% vs. 22.1%) and greater interest in training (58% vs. 38.5%, p < 0.05). Conclusions: Teledentistry shows moderate but increasing adoption among Romanian dentists. Addressing current barriers, through legislative clarification, infrastructure development, targeted professional training, and public education, is essential for achieving sustainable integration into modern dental practice. Full article
(This article belongs to the Special Issue Machine Learning in Medicine and Health: Data, Methods and Applications)
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22 pages, 18081 KB  
Article
Machine Learning-Driven Metabolic Syndrome Prediction: An International Cohort Validation Study
by Zhao Li, Wenzhong Wu and Hyunsik Kang
Healthcare 2024, 12(24), 2527; https://doi.org/10.3390/healthcare12242527 - 13 Dec 2024
Cited by 5 | Viewed by 6364
Abstract
Background/Objectives: This study aimed to develop and validate a machine learning (ML)-based metabolic syndrome (MetS) risk prediction model. Methods: We examined data from 6155 participants of the China Health and Retirement Longitudinal Study (CHARLS) in 2011. The LASSO regression feature selection identified the [...] Read more.
Background/Objectives: This study aimed to develop and validate a machine learning (ML)-based metabolic syndrome (MetS) risk prediction model. Methods: We examined data from 6155 participants of the China Health and Retirement Longitudinal Study (CHARLS) in 2011. The LASSO regression feature selection identified the best MetS predictors. Nine ML-based algorithms were adopted to build predictive models. The model performance was validated using cohort data from the Korea National Health and Nutrition Examination Survey (KNHANES) (n = 5297), the United Kingdom (UK) Biobank (n = 218,781), and the National Health and Nutrition Examination Survey (NHANES) (n = 2549). Results: The multilayer perceptron (MLP)-based model performed best in the CHARLS cohort (AUC = 0.8908; PRAUC = 0.8073), the logistic model in the KNHANES cohort (AUC = 0.9101, PRAUC = 0.8116), the xgboost model in the UK Biobank cohort (AUC = 0.8556, PRAUC = 0.6246), and the MLP model in the NHANES cohort (AUC = 0.9055, PRAUC = 0.8264). Conclusions: Our MLP-based model has the potential to serve as a clinical application for detecting MetS in different populations. Full article
(This article belongs to the Special Issue Machine Learning in Medicine and Health: Data, Methods and Applications)
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