Loo, Wei Kit and Voon, Wingates and Suhaimi, Anwar and Teh, Cindy Shuan Ju and Tee, Yee Kai and Hum, Yan Chai and Hasikin, Khairunnisa and Teo, Kareen and Ong, Hang Cheng and Lai, Khin Wee (2024) Predictive Modeling of COVID-19 Readmissions: Insights from Machine Learning and Deep Learning Approaches. Diagnostics, 14 (14). p. 1511. ISSN 2075-4418, DOI https://doi.org/10.3390/diagnostics14141511.
Full text not available from this repository.Abstract
This project employs artificial intelligence, including machine learning and deep learning, to assess COVID-19 readmission risk in Malaysia. It offers tools to mitigate healthcare resource strain and enhance patient outcomes. This study outlines a methodology for classifying COVID-19 readmissions. It starts with dataset description and pre-processing, while the data balancing was computed through Random Oversampling, Borderline SMOTE, and Adaptive Synthetic Sampling. Nine machine learning and ten deep learning techniques are applied, with five-fold cross-validation for evaluation. Optuna is used for hyperparameter selection, while the consistency in training hyperparameters is maintained. Evaluation metrics encompass accuracy, AUC, and training/inference times. Results were based on stratified five-fold cross-validation and different data-balancing methods. Notably, CatBoost consistently excelled in accuracy and AUC across all tables. Using ROS, CatBoost achieved the highest accuracy (0.9882 +/- 0.0020) with an AUC of 1.0000 +/- 0.0000. CatBoost maintained its superiority in BSMOTE and ADASYN as well. Deep learning approaches performed well, with SAINT leading in ROS and TabNet leading in BSMOTE and ADASYN. Decision Tree ensembles like Random Forest and XGBoost consistently showed strong performance.
| Item Type: | Article |
|---|---|
| Funders: | Impact-Oriented Interdisciplinary Research Grant (IIRG), Universiti Malaya (IIRG001B-2021IISS), The 2020 APT EBC-C (Extra-Budgetary Contributions from China) Project on Promoting the Use of ICT for Achievement of Sustainable Development Goals (IF015-2021) |
| Uncontrolled Keywords: | COVID-19; readmission; prediction; machine learning; deep learning |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering Faculty of Medicine > Medical Microbiology Department Faculty of Medicine > Medicine Department Faculty of Medicine > Rehabilitation Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Mar 2025 08:24 |
| Last Modified: | 24 Mar 2025 08:24 |
| URI: | http://eprints.um.edu.my/id/eprint/46853 |
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