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An interpretable machine learning model to predict hospitalizations

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Název: An interpretable machine learning model to predict hospitalizations
Autoři: Hagar Elbatanouny, Hissam Tawfik, Tarek Khater, Anatoliy Gorbenko
Zdroj: Clinical eHealth, Vol 8, Iss, Pp 53-65 (2025)
Informace o vydavateli: Elsevier BV, 2025.
Rok vydání: 2025
Témata: Interpretable models, Machine learning, Explainable AI, COVID-19, Medicine, Pandemics, Hospital management
Popis: 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.
Druh dokumentu: Article
Jazyk: English
ISSN: 2588-9141
DOI: 10.1016/j.ceh.2025.03.004
Přístupová URL adresa: https://doaj.org/article/e88fb3f78b7345df9c34f537b3e1a805
Rights: CC BY NC ND
Přístupové číslo: edsair.doi.dedup.....95bfb7abefa5e403d83d5bd1bafbcd8a
Databáze: OpenAIRE
Popis
Abstrakt: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.
ISSN:25889141
DOI:10.1016/j.ceh.2025.03.004