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.