{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:15:32Z","timestamp":1790270132374,"version":"4.1.0"},"reference-count":46,"publisher":"Medical Journals Sweden AB","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JRM"],"abstract":"<jats:p>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.\nLAY ABSTRACTCardiac rehabilitation has proven beneficial effects for cardiac patients; it lowers patients\u2019 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.<\/jats:p>","DOI":"10.2340\/jrm.v54.2432","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T15:43:13Z","timestamp":1666971793000},"page":"jrm00348","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models"],"prefix":"10.2340","volume":"55","author":[{"given":"Choo Jia","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kasturi Dewi","family":"Varathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anwar","family":"Suhaimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee Wan","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1183","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"27053","unstructured":"Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. 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