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This paper is in the following e-collection/theme issue:

Machine Learning (2316) Public (e)Health, Digital Epidemiology and Public Health Informatics (1154) Diabetes (295) Obesity and Nutrition as Public Health Problem (456) Diabetes Surveillance and Epidemiology (43) Data Science (408)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64479, first published .
Person on scale with measuring tape for weight loss journey

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study

Authors of this article:

Maik JM Beuken1 Author Orcid Image ;   Melanie Kleynen2 Author Orcid Image ;   Susy Braun2 Author Orcid Image ;   Kees Van Berkel1, 3 Author Orcid Image ;   Carla van der Kallen4, 5 Author Orcid Image ;   Annemarie Koster6, 7 Author Orcid Image ;   Hans Bosma6, 7 Author Orcid Image ;   Tos TJM Berendschot8 Author Orcid Image ;   Alfons JHM Houben4, 5 Author Orcid Image ;   Nicole Dukers-Muijrers9, 10 Author Orcid Image ;   Joop P van den Bergh4, 11 Author Orcid Image ;   Abraham A Kroon4, 5 Author Orcid Image ;   Maastricht Study Management12 ;   Iris M Kanera2 Author Orcid Image
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Journals

  1. Liu P, Guo Z, Sheng Y, Tan L, Li B, Zhang W, Min H, Lyu X. Development and validation of a clinlabomics-based machine-learning model for noninvasive risk stratification of moderate-to-severe OSA. International Journal of Medical Informatics 2026;216:106474 View