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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Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke statistics - 2019 update: a report from the American Heart Association. Circulation 2019; 139: e56-e528.
Heran BS, Chen JM, Ebrahim S, Moxham T, Oldridge N, Rees K, et al. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev 2011; 6: CD001800. https://doi.org/10.1002/14651858.CD001800.pub2
https://doi.org/10.1002/14651858.CD001800.pub2 DOI: https://doi.org/10.1002/14651858.CD001800.pub2
CHIN TS. Malaysians get heart attacks at younger age than others. TheStar Newspaper 2016. [accessed 2022 Nov 7]. Available from: https://www.thestar.com.my/news/nation/2016/08/07/malaysians-get-heart-attacks-at-younger-age-than-others
Cauter JVd, Bacquer DD, Clays E, Smedt DD, Kotseva K, Braeckman L. Return to work and associations with psychosocial well-being and health-related quality of life in coronary heart disease patients: results from EUROASPIRE IV. Eur J Prev Cardiol 2019; 26: 1386-1395. https://doi.org/10.1177/2047487319843079
https://doi.org/10.1177/2047487319843079 DOI: https://doi.org/10.1177/2047487319843079
Du R, Wang P, Ma L, Larcher LM, Wang T, Chen C. Health-related quality of life and associated factors in patients with myocardial infarction after returning to work: a cross-sectional study. Health Qual Life Outcomes 2020; 18: 190. https://doi.org/10.1186/s12955-020-01447-4
https://doi.org/10.1186/s12955-020-01447-4 DOI: https://doi.org/10.1186/s12955-020-01447-4
Warraich HJ, Kaltenbach LA, Fonarow GC, Peterson ED, Wang TY. Adverse change in employment status after acute myocardial infarction: analysis from the TRANSLATE-ACS study. Circ Cardiovasc Qual Outcomes 2018; 11: e004528. https://doi.org/10.1161/CIRCOUTCOMES.117.004528
https://doi.org/10.1161/CIRCOUTCOMES.117.004528 DOI: https://doi.org/10.1161/CIRCOUTCOMES.117.004528
Bresseleers J, De Sutter J. Return to work after acute coronary syndrome: time for action. London: Sage Publications Sage UK; 2019, p. 1355-3557. https://doi.org/10.1177/2047487319840183
https://doi.org/10.1177/2047487319840183 DOI: https://doi.org/10.1177/2047487319840183
Mustafah NM, Kasim S, Isa MR, Hanapiah FA, Abdul Latif L. Predicting return to work following a cardiac event in Malaysia. Work 2017; 58: 481-488. https://doi.org/10.3233/WOR-172646
https://doi.org/10.3233/WOR-172646 DOI: https://doi.org/10.3233/WOR-172646
Salzwedel A, Koran I, Langheim E, Schlitt A, Nothroff J, Bongarth C, et al. Patient-reported outcomes predict return to work and health-related quality of life six months after cardiac rehabilitation: results from a German multi-Centre registry (OutCaRe). PloS One 2020; 15: e0232752. https://doi.org/10.1371/journal.pone.0232752
https://doi.org/10.1371/journal.pone.0232752 DOI: https://doi.org/10.1371/journal.pone.0232752
Salzwedel A, Reibis R, Wegscheider K, Eichler S, Buhlert H, Kaminski S, et al. Cardiopulmonary exercise testing is predictive of return to work in cardiac patients after multicomponent rehabilitation. Clin Res Cardiol 2016; 105: 257-267. https://doi.org/10.1007/s00392-015-0917-1
https://doi.org/10.1007/s00392-015-0917-1 DOI: https://doi.org/10.1007/s00392-015-0917-1
Fiabane E, Argentero P, Calsamiglia G, Candura SM, Giorgi I, Scafa F, et al. Does job satisfaction predict early return to work after coronary angioplasty or cardiac surgery? Int Arch Occup Environ Health 2013; 86: 561-569. https://doi.org/10.1007/s00420-012-0787-z
https://doi.org/10.1007/s00420-012-0787-z DOI: https://doi.org/10.1007/s00420-012-0787-z
Samkange-Zeeb F, Altenhöner T, Berg G, Schott T. Predicting non-return to work in patients attending cardiac rehabilitation. Int J Rehabil Res 2006; 29: 43-49. https://doi.org/10.1097/01.mrr.0000185949.02552.0d
https://doi.org/10.1097/01.mrr.0000185949.02552.0d DOI: https://doi.org/10.1097/01.mrr.0000185949.02552.0d
Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients' view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ 1996; 312: 1191-1194. https://doi.org/10.1136/bmj.312.7040.1191
https://doi.org/10.1136/bmj.312.7040.1191 DOI: https://doi.org/10.1136/bmj.312.7040.1191
Engblom E, Hämäläinen H, Rönnemaa T, Vänttinen E, Kallio V, Knuts L-R. Cardiac rehabilitation and return to work after coronary artery bypass surgery. Qual Life Res 1994; 3: 207-213. https://doi.org/10.1007/BF00435386
https://doi.org/10.1007/BF00435386 DOI: https://doi.org/10.1007/BF00435386
Chandrashekar G, Sahin F. A survey on feature selection methods. Comput Elec Eng 2014; 40: 16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024
https://doi.org/10.1016/j.compeleceng.2013.11.024 DOI: https://doi.org/10.1016/j.compeleceng.2013.11.024
Forman G, Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. Acm Sigkdd Explorations Newsletter 2010; 12: 49-57. https://doi.org/10.1145/1882471.1882479
https://doi.org/10.1145/1882471.1882479 DOI: https://doi.org/10.1145/1882471.1882479
Marcot BG, Hanea AM. What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics 2021; 36: 2009-2031. https://doi.org/10.1007/s00180-020-00999-9
https://doi.org/10.1007/s00180-020-00999-9 DOI: https://doi.org/10.1007/s00180-020-00999-9
Stevens, B, Pezzulloa L, Verdiana L, Tomlinsona J, Estrada-Aguilara C, Georgea A, Verdejo-París J. The economic burden of hypertension, heart failure, myocardial infarction, and atrial fibrillation in Mexico. Archivos de cardiología de México 2018; 88(3): 241-244.
https://doi.org/10.1016/j.acmx.2018.03.004 DOI: https://doi.org/10.1016/j.acmx.2018.03.004
Eijsvogels, Thijs MH, Martijn FHM, Esmee AB, Esther PM, Niels van Gorp, et al. Association of cardiac rehabilitation with all-cause mortality among patients with cardiovascular disease in the Netherlands. JAMA Network Open 2020; 7: e2011686-e2011686
https://doi.org/10.1001/jamanetworkopen.2020.11686 DOI: https://doi.org/10.1001/jamanetworkopen.2020.11686
Teo, Koon K, Talha R. Cardiovascular risk factors and prevention: a perspective from developing countries. Canadian Journal of Cardiology 37.5 2021; 733-743.
https://doi.org/10.1016/j.cjca.2021.02.009 DOI: https://doi.org/10.1016/j.cjca.2021.02.009
Neil B, Oldridge, Maureen T, Pakosh, Randal J, Thomas. Cardiac rehabilitation in low- and middle-income countries: a review on cost and cost-effectiveness. International Health; 8: 77-82. https://doi.org/10.1093/inthealth/ihv047
https://doi.org/10.1093/inthealth/ihv047 DOI: https://doi.org/10.1093/inthealth/ihv047
Ragupathi L, Stribling J, Yakunina Y, Fuster V, McLaughlin MA, Vedanthan R. Availability, Use, and Barriers to Cardiac Rehabilitation in LMIC. Glob Heart 2017; 12: 323-334. https://doi.org/10.1016/j.gheart.2016.09.004
https://doi.org/10.1016/j.gheart.2016.09.004 DOI: https://doi.org/10.1016/j.gheart.2016.09.004
Pesah E, Turk-Adawi K, Supervia M, Lopez-Jimenez F, Britto R, Ding R, et al. Cardiac rehabilitation delivery in low/middle-income countries. Heart 2019; 105: 1806-1812. https://doi.org/10.1136/heartjnl-2018-314486
https://doi.org/10.1136/heartjnl-2018-314486 DOI: https://doi.org/10.1136/heartjnl-2018-314486
Bakhshayeh, Samaneh, et al. Barriers to participation in center-based cardiac rehabilitation programs and patients' attitude toward home-based cardiac rehabilitation programs. Physiotherapy Theory and Practice 2019.
https://doi.org/10.1080/09593985.2019.1620388 DOI: https://doi.org/10.1080/09593985.2019.1620388
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Copyright (c) 2022 Choo Jia Yuan, Kasturi Dewi Varathan, Anwar Suhaimi, Lee Wan Ling
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