Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.

Journal: BMC medical informatics and decision making
PMID: 39334279

Abstract

BACKGROUND: In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aimed to apply ML methods on extensive national-level health and social administrative datasets to assess the utility of these for predicting future diabetes complications, including by ethnicity.

Authors

  • Nhung Nghiem
    Department of Public Health, University of Otago, 23A Mein Street, Wellington 6021, New Zealand.
  • Nick Wilson
    Department of Public Health, University of Otago Wellington, Wellington City, Wellington, 6021, New Zealand.
  • Jeremy Krebs
    Department of Medicine, University of Otago Wellington, Wellington City, Wellington, 6021, New Zealand.
  • Truyen Tran
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.

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