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

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

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:International Journal of Environmental Research and Public Health
Subjects:
artificial intelligence
machine learning
clinical decision support
cardiovascular disease
cardiovascular risk factors
risk prediction
Online Access:https://www.mdpi.com/1660-4601/18/6/3187

Internet

https://www.mdpi.com/1660-4601/18/6/3187

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