Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

• Published in: Journal of rehabilitation medicine Vol. 55; p. jrm00348
• Main Authors: Yuan, Choo Jia, Varathan, Kasturi Dewi, Suhaimi, Anwar, Ling, Lee Wan
• Format: Journal Article
• Language: English
• Published: Sweden Journal of Rehabilitation Medicine 09.01.2023; Medical Journals Sweden
• Subjects: Cardiac Rehabilitation; Cardiovascular disease; Cardiovascular diseases; Extraction; feature selection; Humans; Logistic Models; Machine Learning; Patient admissions; Prediction models; Rehabilitation; Return to Work; ROC Curve
• ISSN: 1651-2081; 1650-1977; 1651-2081
• DOI: 10.2340/jrm.v54.2432

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 ABSTRACTCardiac 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.