Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models.

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.