Journal article

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

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | MDPI | Published : 2021

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..

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Grants

Awarded by Australian National Health and Medical Research Council


Funding Acknowledgements

This research was funded by the Government of South Australia and the Shandong Provincial Government, China. The funders had no role in the study design, decision to publish, or preparation of the manuscript. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414, and 1074383 and by infrastructure provided by Cancer Council Victoria.