Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models
DOI:
https://doi.org/10.2340/jrm.v54.2432Keywords:
cardiac rehabilitation, machine learning, return to work, feature selectionAbstract
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.
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Copyright (c) 2022 Choo Jia Yuan, Kasturi Dewi Varathan, Anwar Suhaimi, Lee Wan Ling

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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for non-commercial purposes, provided proper attribution to the original work.