Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

Journal: PloS one
Published Date: Jan 1, 2019

Abstract

BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions.

Authors

  • Ahmed M Alaa
    Department of Electrical Engineering, University of California, Los Angeles, CA, 90095, USA. ahmedmalaa@ucla.edu.
  • Thomas Bolton
    Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
  • Emanuele Di Angelantonio
    Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
  • James H F Rudd
    Department of Cardiovascular Medicine, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.

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