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

Shelda Sajeev,Alline Beleigoli,Tiffany K Gill,Jonathan E Shaw,Sarah Appleton,Roger L Milne,Derek Chew,Stephanie Champion,Dianna J Magliano,Richard L Reed,Anthony Maeder

doi:10.3390/ijerph18063187

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.

Highlights

IntroductionMany cardiovascular disease risk factors are modifiable and, with early diagnosis and intervention of individuals at higher risk, CVD mortality and morbidities are largely preventable [2]
Cardiovascular disease (CVD) is the leading cause of death in Australia [1]
For the North West Adelaide Health Study (NWAHS) and AusDiab cohorts, all four of the Machine learning (ML) models achieved significantly better performance than the Framingham model for predicting cardiovascular disease (CVD) deaths

Summary

Introduction

Many cardiovascular disease risk factors are modifiable and, with early diagnosis and intervention of individuals at higher risk, CVD mortality and morbidities are largely preventable [2]. The Framingham Risk Score, one of the most commonly used and widely validated models worldwide, is derived from a largely Caucasian population of European descent, and may be less accurate for some high-risk groups, such as individuals with diabetes, socio-economically disadvantaged populations [6], and Australian females [7]

Methods

Results

Discussion

Conclusion