Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/130615
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Predicting Australian adults at high risk of cardiovascular disease mortality using standard risk factors and machine learning
Author: Sajeev, S.
Champion, S.
Beleigoli, A.
Chew, D.
Reed, R.L.
Magliano, D.J.
Shaw, J.E.
Milne, R.L.
Appleton, S.
Gill, T.K.
Maeder, A.
Citation: International Journal of Environmental Research and Public Health, 2021; 18(6):1-14
Publisher: MDPI AG
Issue Date: 2021
ISSN: 1661-7827
1660-4601
Statement of
Responsibility: 
Shelda Sajeev, Stephanie Champion, Alline Beleigoli, Derek Chew, Richard L. Reed, Dianna J. Magliano ... et al.
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.
Keywords: Artificial intelligence; machine learning; clinical decision support; cardiovascular disease; cardiovascular risk factors; risk prediction
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
DOI: 10.3390/ijerph18063187
Grant ID: http://purl.org/au-research/grants/nhmrc/1074383
http://purl.org/au-research/grants/nhmrc/396414
http://purl.org/au-research/grants/nhmrc/1074383
Published version: http://dx.doi.org/10.3390/ijerph18063187
Appears in Collections:Aurora harvest 8
Medicine publications

Files in This Item:
File Description SizeFormat 
hdl_130615.pdf483.75 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.