ArticleInternational journal of environmental research and public health2021
Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.Open heart · 2026Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Predicting non-emergency healthcare use in Australia using machine learning on longitudinal household data.Scientific reports · 2026Article
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
- Enhancing cardiovascular risk prediction in Asian populations: A machine learning approach integrated with digital health platforms.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population.PloS one · 2025Article
- Prediction of cardiovascular risk using machine-learning methods. Sex-specific differences.Frontiers in cardiovascular medicine · 2025Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach.Journal of inflammation research · 2024Article
- Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum.Journal of medical Internet research · 2023Article
- Article
- Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches.International journal of environmental research and public health · 2021Article
- Post-Analysis of Predictive Modeling with an Epidemiological Example.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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