Using machine learning to predict cardiovascular risk using self-reported questionnaires: Findings from the 45 and Up Study.

Autor: Wang H; School of Computer Science & Engineering, University of New South Wales, Sydney, NSW, Australia., Tucker WJ; School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia., Jonnagaddala J; School of Population Health, University of New South Wales, Sydney, NSW, Australia., Schutte AE; School of Population Health, University of New South Wales, Sydney, NSW, Australia; The George Institute for Global Health, Sydney, NSW, Australia., Jalaludin B; School of Population Health, University of New South Wales, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, University of New South Wales, Sydney, Australia., Liaw ST; WHO Collaborating Centre for eHealth, School of Population Health, University of New South Wales, Sydney, NSW, Australia., Rye KA; School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia., Wong RK; School of Computer Science & Engineering, University of New South Wales, Sydney, NSW, Australia. Electronic address: wong@cse.unsw.edu.au., Ong KL; School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia; NHMRC Clinical Trials Centre, Medical Foundation Building, The University of Sydney, 92-94 Parramatta Road, Camperdown, NSW 2050, Australia. Electronic address: oklws@yahoo.com.hk.
Jazyk: angličtina
Zdroj: International journal of cardiology [Int J Cardiol] 2023 Sep 01; Vol. 386, pp. 149-156. Date of Electronic Publication: 2023 May 19.
DOI: 10.1016/j.ijcard.2023.05.030
Abstrakt: Background: Machine learning has been shown to outperform traditional statistical methods for risk prediction model development. We aimed to develop machine learning-based risk prediction models for cardiovascular mortality and hospitalisation for ischemic heart disease (IHD) using self-reported questionnaire data.
Methods: The 45 and Up Study was a retrospective population-based study in New South Wales, Australia (2005-2009). Self-reported healthcare survey data on 187,268 participants without a history of cardiovascular disease was linked to hospitalisation and mortality data. We compared different machine learning algorithms, including traditional classification methods (support vector machine (SVM), neural network, random forest and logistic regression) and survival methods (fast survival SVM, Cox regression and random survival forest).
Results: A total of 3687 participants experienced cardiovascular mortality and 12,841 participants had IHD-related hospitalisation over a median follow-up of 10.4 years and 11.6 years respectively. The best model for cardiovascular mortality was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 0.3 achieved by under-sampling of the non-cases. This model had the Uno's and Harrel's concordance indexes of 0.898 and 0.900 respectively. The best model for IHD hospitalisation was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 1.0 with Uno's and Harrel's concordance indexes of 0.711 and 0.718 respectively.
Conclusion: Machine learning-based risk prediction models developed using self-reported questionnaire data had good prediction performance. These models may have the potential to be used in initial screening tests to identify high-risk individuals before undergoing costly investigation.
Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to disclose.
(Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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