Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

Abstract

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. LAY ABSTRACT 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.

Keywords

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