TY - JOUR
T1 - Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning
AU - Sajeev, Shelda
AU - Champion, Stephanie
AU - Beleigoli, Alline
AU - Chew, Derek
AU - Reed, Richard L.
AU - Magliano, Dianna J.
AU - Shaw, Jonathan E.
AU - Milne, Roger L.
AU - Appleton, Sarah
AU - Gill, Tiffany K.
AU - Maeder, Anthony
PY - 2021/3/2
Y1 - 2021/3/2
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cardiovascular disease
KW - Cardiovascular risk factors
KW - Clinical decision support
KW - Machine learning
KW - Risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85102712843&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/NHMRC/209057
UR - http://purl.org/au-research/grants/NHMRC/396414
UR - http://purl.org/au-research/grants/NHMRC/1074383
U2 - 10.3390/ijerph18063187
DO - 10.3390/ijerph18063187
M3 - Article
AN - SCOPUS:85102712843
SN - 1661-7827
VL - 18
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 6
M1 - 3187
ER -