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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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Autores principales: Sajeev, Shelda, Champion, Stephanie, Beleigoli, Alline, Chew, Derek, Reed, Richard L., Magliano, Dianna J., Shaw, Jonathan E., Milne, Roger L., Appleton, Sarah, Gill, Tiffany K., Maeder, Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003399/
https://www.ncbi.nlm.nih.gov/pubmed/33808743
http://dx.doi.org/10.3390/ijerph18063187
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author Sajeev, Shelda
Champion, Stephanie
Beleigoli, Alline
Chew, Derek
Reed, Richard L.
Magliano, Dianna J.
Shaw, Jonathan E.
Milne, Roger L.
Appleton, Sarah
Gill, Tiffany K.
Maeder, Anthony
author_facet Sajeev, Shelda
Champion, Stephanie
Beleigoli, Alline
Chew, Derek
Reed, Richard L.
Magliano, Dianna J.
Shaw, Jonathan E.
Milne, Roger L.
Appleton, Sarah
Gill, Tiffany K.
Maeder, Anthony
author_sort Sajeev, Shelda
collection PubMed
description 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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spelling pubmed-80033992021-03-28 Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning Sajeev, Shelda Champion, Stephanie Beleigoli, Alline Chew, Derek Reed, Richard L. Magliano, Dianna J. Shaw, Jonathan E. Milne, Roger L. Appleton, Sarah Gill, Tiffany K. Maeder, Anthony Int J Environ Res Public Health Article 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. MDPI 2021-03-19 /pmc/articles/PMC8003399/ /pubmed/33808743 http://dx.doi.org/10.3390/ijerph18063187 Text en © 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sajeev, Shelda
Champion, Stephanie
Beleigoli, Alline
Chew, Derek
Reed, Richard L.
Magliano, Dianna J.
Shaw, Jonathan E.
Milne, Roger L.
Appleton, Sarah
Gill, Tiffany K.
Maeder, Anthony
Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title_full Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title_fullStr Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title_full_unstemmed Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title_short Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
title_sort predicting australian adults at high risk of cardiovascular disease mortality using standard risk factors and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003399/
https://www.ncbi.nlm.nih.gov/pubmed/33808743
http://dx.doi.org/10.3390/ijerph18063187
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