Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

Journal: PloS one
PMID: 35390039

Abstract

OBJECTIVE: We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.

Authors

  • Mohammad Ziaul Islam Chowdhury
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Iffat Naeem
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Hude Quan
    Department of Community Health Sciences, University of Calgary, Calgary, Canada.
  • Alexander A Leung
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Khokan C Sikdar
    Health Status Assessment, Surveillance, and Reporting, Public Health Surveillance and Infrastructure, Population, Public and Indigenous Health, Alberta Health Services, Calgary, Alberta, Canada.
  • Maeve O'Beirne
    Department of Family Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Tanvir C Turin
    Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

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