Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning

Shelda Sajeev, Stephanie Champion, Alline Beleigoli, Derek Chew, Richard L. Reed, Dianna J. Magliano, Jonathan E. Shaw, Roger L. Milne, Sarah Appleton, Tiffany K. Gill and Anthony Maeder
Additional contact information
Shelda Sajeev: Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia
Stephanie Champion: Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia
Alline Beleigoli: Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia
Derek Chew: College of Medicine and Public Health, Flinders University, Adelaide, SA 5001, Australia
Richard L. Reed: College of Medicine and Public Health, Flinders University, Adelaide, SA 5001, Australia
Dianna J. Magliano: Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
Jonathan E. Shaw: Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
Roger L. Milne: Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
Sarah Appleton: Flinders Health and Medical Research Institute (Sleep Health)/Adelaide Institute for Sleep Health (AISH), College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
Tiffany K. Gill: Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
Anthony Maeder: Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia

IJERPH, 2021, vol. 18, issue 6, 1-14

Abstract: Effective cardiovascular disease (CVD) prevention relies on timely identification and intervention for individuals at risk. Conventional formula-based techniques have been demonstrated to over- or under-predict the risk of CVD in the Australian population. This study assessed the ability of machine learning models to predict CVD mortality risk in the Australian population and compare performance with the well-established Framingham model. Data is drawn from three Australian cohort studies: the North West Adelaide Health Study (NWAHS), the Australian Diabetes, Obesity, and Lifestyle study, and the Melbourne Collaborative Cohort Study (MCCS). Four machine learning models for predicting 15-year CVD mortality risk were developed and compared to the 2008 Framingham model. Machine learning models performed significantly better compared to the Framingham model when applied to the three Australian cohorts. Machine learning based models improved prediction by 2.7% to 5.2% across three Australian cohorts. In an aggregated cohort, machine learning models improved prediction by up to 5.1% (area-under-curve (AUC) 0.852, 95% CI 0.837–0.867). Net reclassification improvement (NRI) was up to 26% with machine learning models. Machine learning based models also showed improved performance when stratified by sex and diabetes status. Results suggest a potential for improving CVD risk prediction in the Australian population using machine learning models.

Keywords: artificial intelligence; machine learning; clinical decision support; cardiovascular disease; cardiovascular risk factors; risk prediction (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/6/3187/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/6/3187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:6:p:3187-:d:520495

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().