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Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

Yuan CJ 1 , Varathan KD 2 , Suhaimi A 3 , Ling LW 4

Affiliations 

  • 1 Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
  • 2 Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia. kasturi@um.edu.my
  • 3 Department of Rehabilitation Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia. anwar@ummc.edu.my
  • 4 Department of Nursing Science, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
J Rehabil Med, 2023 Jan 09;55:jrm00348.
PMID: 36306152 DOI: 10.2340/jrm.v54.2432

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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