Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model
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 significan...
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MDPI AG
2025-01-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/1/13 |
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| author | A. M. Mutawa |
| author_facet | A. M. Mutawa |
| author_sort | A. M. Mutawa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-16a5817aad0447d590b753bc3c51dba5 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-16a5817aad0447d590b753bc3c51dba52025-01-24T13:22:33ZengMDPI AGBig Data and Cognitive Computing2504-22892025-01-01911310.3390/bdcc9010013Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning ModelA. M. Mutawa0Computer Engineering Department, Kuwait University, Safat 13060, KuwaitIntensive 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.https://www.mdpi.com/2504-2289/9/1/13COVID-19feature selectionintensive care unitmachine learningSMOTESHAP |
| spellingShingle | A. M. Mutawa Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model Big Data and Cognitive Computing COVID-19 feature selection intensive care unit machine learning SMOTE SHAP |
| title | Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model |
| title_full | Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model |
| title_fullStr | Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model |
| title_full_unstemmed | Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model |
| title_short | Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model |
| title_sort | predicting intensive care unit admissions in covid 19 patients an ai powered machine learning model |
| topic | COVID-19 feature selection intensive care unit machine learning SMOTE SHAP |
| url | https://www.mdpi.com/2504-2289/9/1/13 |
| work_keys_str_mv | AT ammutawa predictingintensivecareunitadmissionsincovid19patientsanaipoweredmachinelearningmodel |