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

Author

Listed:
  • Shelda Sajeev

    (Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia
    Chifley Business School, Torrens University, Australia, Adelaide, SA 5000, Australia)

  • Stephanie Champion

    (Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia)

  • Alline Beleigoli

    (Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia
    Caring Futures Institute, Flinders University, Adelaide, SA 5001, Australia)

  • Derek Chew

    (College of Medicine and Public Health, Flinders University, Adelaide, SA 5001, Australia)

  • Richard L. Reed

    (College of Medicine and Public Health, Flinders University, Adelaide, SA 5001, Australia)

  • Dianna J. Magliano

    (Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
    School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia)

  • Jonathan E. Shaw

    (Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
    School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
    School of Life Sciences, La Trobe University, Melbourne, VIC 3086, Australia)

  • Roger L. Milne

    (Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Melbourne, VIC 3010, Australia
    Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia)

  • Sarah Appleton

    (Flinders Health and Medical Research Institute (Sleep Health)/Adelaide Institute for Sleep Health (AISH), College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
    Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia)

  • Tiffany K. Gill

    (Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia)

  • Anthony Maeder

    (Flinders Digital Health Research Centre, College of Nursing & Health Sciences, Flinders University, Adelaide SA 5001, Australia)

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.

Suggested Citation

  • Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3187-:d:520495
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    References listed on IDEAS

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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    2. Ahmed M Alaa & Thomas Bolton & Emanuele Di Angelantonio & James H F Rudd & Mihaela van der Schaar, 2019. "Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-17, May.
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    1. Mirza Rizwan Sajid & Bader A. Almehmadi & Waqas Sami & Mansour K. Alzahrani & Noryanti Muhammad & Christophe Chesneau & Asif Hanif & Arshad Ali Khan & Ahmad Shahbaz, 2021. "Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches," IJERPH, MDPI, vol. 18(23), pages 1-16, November.

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