Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models


  • Choo Jia Yuan Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
  • Kasturi Dewi Varathan Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
  • Anwar Suhaimi Department of Rehabilitation Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
  • Lee Wan Ling Department of Nursing Science, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia



cardiac rehabilitation, machine learning, return to work, feature selection


Objective: To explore machine learning models for predicting return to work after cardiac rehabilitation.
Subjects: Patients who were admitted to the University 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 features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
Results: The AdaBoost model with the top 20 features 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.

Cardiac rehabilitation has proven beneficial effects for cardiac patients; it lowers patients’ risk of cardiac death and improves their health-related quality of life. Returning to work is one of the important goals of cardiac rehabilitation, as it prevents early retirement, and encourages social and financial sustainability. A few studies have focussed on predicting return to work among cardiac rehabilitation patients; however, these studies have only used statistical techniques in their prediction. This study showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.


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How to Cite

Yuan, C. J. ., Varathan, K. D., Suhaimi, A., & Ling, L. W. (2023). Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models. Journal of Rehabilitation Medicine, 55, jrm00348.



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