Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models
Keywords:cardiac rehabilitation, machine learning, return to work, feature selection
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.
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.
How to Cite
Copyright (c) 2022 Choo Jia Yuan, Kasturi Dewi Varathan, Anwar Suhaimi, Lee Wan Ling
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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.