Yuan, Choo Jia and Varathan, Kasturi Dewi and Suhaimi, Anwar and Ling, Lee Wan (2022) Predicting return to work after cardiac rehabilitation using machine learning models. Journal of Rehabilitation Medicine, 55. ISSN 1650-1977, DOI https://doi.org/10.2340/jrm.v54.2432.
Full text not available from this repository.Abstract
Objective: To explore machine learning models for predicting return to work after cardiac rehabilitation. Subjects: Patients who were admitted to the Univer-sity of Malaya Medical Centre due to cardiac events.Methods: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant featu-res from multiple logistic regression; and features selected from recursive feature extraction techni-que. The performance of the prediction models with each set of features was compared.Results: The AdaBoost model with the top 20 fea-tures obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.Conclusion: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.
| Item Type: | Article |
|---|---|
| Funders: | Prototype Research Grant Scheme [Grant No: PRGS/1/2022/SKK01/UM/02/1-PR001-2022], University of Malaya Research Grant [Grant No: RF009C-2018], University of Malaya Specialist Center Care Fund |
| Uncontrolled Keywords: | Cardiac rehabilitation; Machine learning; Return to work; Feature selection |
| Subjects: | R Medicine > RC Internal medicine > RC1200 Sports Medicine |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Nov 2025 13:34 |
| Last Modified: | 03 Nov 2025 13:34 |
| URI: | http://eprints.um.edu.my/id/eprint/40494 |
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