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, Anthony Maeder

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
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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.

Original languageEnglish
Article number3187
Number of pages14
JournalInternational Journal of Environmental Research and Public Health
Issue number6
Publication statusPublished - 2 Mar 2021


  • Artificial intelligence
  • Cardiovascular disease
  • Cardiovascular risk factors
  • Clinical decision support
  • Machine learning
  • Risk prediction


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