An interpretable machine learning model to predict hospitalizations
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 pandemic...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Clinical eHealth |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2588914125000140 |
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| author | Hagar Elbatanouny Hissam Tawfik Tarek Khater Anatoliy Gorbenko |
| author_facet | Hagar Elbatanouny Hissam Tawfik Tarek Khater Anatoliy Gorbenko |
| author_sort | Hagar Elbatanouny |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e88fb3f78b7345df9c34f537b3e1a805 |
| institution | DOAJ |
| issn | 2588-9141 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Clinical eHealth |
| spelling | doaj-art-e88fb3f78b7345df9c34f537b3e1a8052025-08-20T03:05:17ZengKeAi Communications Co., Ltd.Clinical eHealth2588-91412025-12-018536510.1016/j.ceh.2025.03.004An interpretable machine learning model to predict hospitalizationsHagar Elbatanouny0Hissam Tawfik1Tarek Khater2Anatoliy Gorbenko3Department of Electrical Engineering, University of Sharjah, Sharjah, the United Arab EmiratesDepartment of Electrical Engineering, University of Sharjah, Sharjah, the United Arab Emirates; School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, UK; Corresponding author at: Department of Electrical Engineering, University of Sharjah, Sharjah, the United Arab Emirates.Department of Biomedical Engineering, Khalifa University, Abu Dhabi, the United Arab EmiratesSchool of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, UKHospital 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.http://www.sciencedirect.com/science/article/pii/S2588914125000140Hospital managementPandemicsMachine learningExplainable AICOVID-19Interpretable models |
| spellingShingle | Hagar Elbatanouny Hissam Tawfik Tarek Khater Anatoliy Gorbenko An interpretable machine learning model to predict hospitalizations Clinical eHealth Hospital management Pandemics Machine learning Explainable AI COVID-19 Interpretable models |
| title | An interpretable machine learning model to predict hospitalizations |
| title_full | An interpretable machine learning model to predict hospitalizations |
| title_fullStr | An interpretable machine learning model to predict hospitalizations |
| title_full_unstemmed | An interpretable machine learning model to predict hospitalizations |
| title_short | An interpretable machine learning model to predict hospitalizations |
| title_sort | interpretable machine learning model to predict hospitalizations |
| topic | Hospital management Pandemics Machine learning Explainable AI COVID-19 Interpretable models |
| url | http://www.sciencedirect.com/science/article/pii/S2588914125000140 |
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