Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study
The results indicate Naïve Bayes model to be useful and accurate in identifying MetS in hospital employees independent of gender. The early prediction of MetS using a model that accounts for gender differences is an important part of routine health screening and requires a multidimensional approach, including self-administered questionnaires and anthropometric and biochemical measurements.
Authors
- Yi‐Syuan Wu (ORCID: https://orcid.org/0000-0001-9609-6489)
- Wen‐Chii Tzeng (ORCID: https://orcid.org/0000-0002-4205-896X)
- Hao-Yi Wu (ORCID: https://orcid.org/0000-0001-8689-1075)
- Wei‐Yun Wang (ORCID: https://orcid.org/0000-0002-9783-3302)
- Cui Kang
- Cheng-Wei Wu (ORCID: https://orcid.org/0000-0002-5656-015X)
Institutions
- Tri-Service General Hospital (TW)
- National Taitung University (TW)
- National Defense Medical Center (TW)
- National Ilan University (TW)
Publication Details
- Journal
- Journal of Nursing Research
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1097/jnr.0000000000000668
- Primary Topic
- Health Promotion and Cardiovascular Prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00