[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120289-en":3,"doc-seo-120289-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120289,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","An interpretable machine learning model to predict hospitalizations - Hospitalization prediction during pandemics","Hospital management plays a pivotal role in ensuring efficient delivery of medical services, particularly during pandemics such as COVID-19. This paper applies interpretable machine learning to hospitalizations using a comprehensive dataset from the Mexican government. Supervised models including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated. Feature importance and dimensionality reduction support predictive performance, with Gradient Boosting achieving 85.63% accuracy and AUC 0.8696. Interpretability plots highlight pneumonia’s positive effect and identify the highest hospitalization likelihood in women over 45 with pneumonia and concurrent COVID-19.","Citation:  \nElbatanouny, H and Tawfik, H and Khater, T and Gorbenko, A (2025) An interpretable machine learning model to predict hospitalizations. Clinical eHealth, 8 . pp. 53-65. ISSN 2588-9141 DOI: [https://doi.org/10.1016/j.ceh.2025.03.004](https://doi.org/10.1016/j.ceh.2025.03.004)  \nLink to Leeds Beckett Repository record:  \n[https://eprints.leedsbeckett.ac.uk/id/eprint/12069/](https://eprints.leedsbeckett.ac.uk/id/eprint/12069/)  \nDocument Version:  \nArticle (Published Version)  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \n© 2025 The Authors  \nThe aim of the Leeds Beckett Repository is to provide open access to our research, as required by funder policies and permitted by publishers and copyright law.  \nThe Leeds Beckett repository holds a wide range of publications, each of which has been checked for copyright and the relevant embargo period has been applied by the Research Services team.  \nWe operate on a standard take-down policy. If you are the author or publisher of an output and you would like it removed from the repository, please contact us and we will investigate on a case-by-case basis.  \nEach thesis in the repository has been cleared where necessary by the author for third party copyright. If you would like a thesis to be removed from the repository or believe there is an issue with copyright, please contact us on [openaccess@leedsbeckett.ac.uk](openaccess@leedsbeckett.ac.uk) and we will investigate on a case-by-case basis.  \nClinical eHealth 8 (2025) 53–65  \nAn interpretable machine learning model to predict hospitalizations Hagar Elbatanouny a, Hissam Tawfik a,b,⁎, Tarek Khater c, Anatoliy Gorbenko b  \na Department of Electrical Engineering, University of Sharjah, Sharjah, the United Arab Emirates b School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, UK c Department of Biomedical Engineering, Khalifa University, Abu Dhabi, the United Arab Emirates  \n\n| a r t i c l e i n f o |  | a b s t r a c t |\n| --- | --- | --- |\n| Article history:\u003Cbr>Received 1 August 2024\u003Cbr>Accepted 30 March 2025 Available online 4 April 2025 |  | Hospital management plays a pivotal role in ensuring the efficient delivery of medical services, especially in the face of challenges posed by pandemics such as COVID-19. This paper explores the application of machine learning techniques in addressing the challenge of hospitalization during pandemics. Leveraging a comprehensive dataset sourced from the Mexican government, various supervised learning algorithms including Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron are trained and evaluated to discern factors contributing to hospitalizations. Feature importance analysis and dimensionality reduction techniques are employed to enhance models predictive performance. The best model was Gradient Boosting algorithm with an accuracy of 85.63% and AUC score of 0.8696. The interpretability plots showed that pneumonia had a positive impact on the hospitalization prediction of the model. Our analysis indicates that women aged over 45 with pneumonia and concurrent COVID-19 exhibit the highest likelihood of hospitalization. This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics.\u003Cbr>© 2025 The Authors. Publishing services by Elsevier B.V. on behalf ofKeAi Communications Co. Ltd. This isan open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)). |\n| Keywords:\u003Cbr>Hospital management Pandemics\u003Cbr>Machine learning Explainable AI COVID-19\u003Cbr>Interpretable models |  |  |\n\n1. Introduction  \nHospitals are complicated establishments that require optimal performance in terms of service quality, service time, costeffective rates, use of supplies, and overal","cbCaimZLaXiW8VVr","https://ap.wps.com/l/cbCaimZLaXiW8VVr","pdf",1797180,1,14,"English","en",105,"# Abstract\n# Introduction\n## Hospital management challenges during pandemics\n## Motivation for machine learning and interpretability","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses predicting hospitalizations during pandemics, where hospital resources and decision-making face major uncertainty.\"},{\"question\":\"Which machine learning methods are evaluated?\",\"answer\":\"It evaluates several supervised algorithms: Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron.\"},{\"question\":\"What are the key interpretability findings?\",\"answer\":\"Interpretability plots show that pneumonia positively impacts hospitalization prediction, and the highest likelihood is for women over 45 with pneumonia and concurrent COVID-19.\"}]","An interpretable machine learning model to predict hospitalizations - 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