{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:13:06Z","timestamp":1759921986065,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage of resources. The study aimed to improve resource management by quickly and accurately identifying patients who need ICU admission. We designed an intelligent decision support system that employs machine learning (ML) to anticipate COVID-19 ICU admissions in Kuwait. Our algorithm examines several clinical and demographic characteristics to identify high-risk individuals early in illness diagnosis. We used 4399 patients to identify ICU admission with predictors such as shortness of breath, high D-dimer values, and abnormal chest X-rays. Any data imbalance was addressed by employing cross-validation along with the Synthetic Minority Oversampling Technique (SMOTE), the feature selection was refined using backward elimination, and the model interpretability was improved using Shapley Additive Explanations (SHAP). We employed various ML classifiers, including support vector machines (SVM). The SVM model surpasses all other models in terms of precision (0.99) and area under curve (AUC, 0.91). This study investigated the healthcare process during a pandemic, facilitating ML-based decision-making solutions to confront healthcare problems.<\/jats:p>","DOI":"10.3390\/bdcc9010013","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:28:31Z","timestamp":1736854111000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5707-2692","authenticated-orcid":false,"given":"A. M.","family":"Mutawa","sequence":"first","affiliation":[{"name":"Computer Engineering Department, Kuwait University, Safat 13060, Kuwait"},{"name":"Computer Sciences Department, University of Hamburg, 22527 Hamburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","unstructured":"Worldometer (2022, July 27). COVID Live\u2014Coronavirus Statistics. Available online: https:\/\/www.worldometers.info\/coronavirus\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/S2213-2600(20)30580-4","article-title":"Allocating scarce intensive care resources during the COVID-19 pandemic: Practical challenges to theoretical frameworks","volume":"9","author":"Supady","year":"2021","journal-title":"Lancet Respir. Med."},{"key":"ref_3","unstructured":"Cascella, M., Rajnik, M., Aleem, A., Dulebohn, S., and Di Napoli, R. (2023, August 02). Features, Evaluation, and Treatment of Coronavirus (COVID-19). 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