Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study
This study demonstrates that a hypothesis-free and fully data-driven approach can be used to identify distinguishable participant clusters in large and complex datasets and find relevant variables that differ within populations with obesity.
Authors
- Maik JM Beuken (ORCID: https://orcid.org/0000-0002-8356-2716)
- Alfons J.H.M. Houben (ORCID: https://orcid.org/0000-0002-1747-8452)
- Nicole H. T. M. Dukers–Muijrers (ORCID: https://orcid.org/0000-0003-4896-758X)
- Tos T. J. M. Berendschot (ORCID: https://orcid.org/0000-0002-8101-939X)
- Melanie Kleynen (ORCID: https://orcid.org/0000-0002-6543-6994)
- Susy Braun (ORCID: https://orcid.org/0000-0002-3037-3428)
- Joop P. van den Bergh (ORCID: https://orcid.org/0000-0003-3984-2232)
- Hans Bosma (ORCID: https://orcid.org/0000-0003-4333-4564)
- Abraham A. Kroon (ORCID: https://orcid.org/0000-0001-7750-8249)
- Carla Kallen (ORCID: https://orcid.org/0000-0003-1468-8793)
- Annemarie Koster (ORCID: https://orcid.org/0000-0003-1583-7391)
- Kees Van Berkel (ORCID: https://orcid.org/0000-0003-0139-9298)
Institutions
- Centraal Bureau voor de Statistiek (NL)
- Zuyd University of Applied Sciences (NL)
- Maastricht University (NL)
- VieCuri Medisch Centrum (NL)
- GGD Zuid Limburg (NL)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-02-05
- DOI
- https://doi.org/10.2196/64479
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00