Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

• Main Authors: Choo Jia Yuan (Author), Kasturi Dewi Varathan (Author), Anwar Suhaimi (Author), Lee Wan Ling (Author)
• Format: Book
• Published: Medical Journals Sweden, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

10.2340/jrm.v54.2432
1651-2081