aceExternal validation and application of machine learning models for obesity in Chinese children and adolescents per Chinese and WHO growth standards
Background Childhood and adolescent obesity is a growing global public health challenge, underscoring the need for reliable tools to identify individuals at high risk. We aimed to develop, validate, and deploy machine learning (ML) models for predicting obesity in children and adolescents aged 3-18 years using multi-domain risk factors per Chinese and World Health Organization (WHO) growth standards, with an online tool for clinical and public health translation. Methods We conducted a population-based cross-sec…
Models built on data from Beijing and tested in Tangshan identified obesity in children aged 3-18 using only 5 to 12 features, and were released as online tools for clinical and public health use.
Discrimination was modest, with AUROCs around 0.70 to 0.74, and was lower in the external Tangshan validation than in the Beijing data.
Evidence
- Peer-reviewedJournal of Global Health2026-10-19
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Truvace Impact Record TRV-2026-2058, v1: “External validation and application of machine learning models for obesity in Chinese children and adolescents per Chinese and WHO growth standards.” Truvace, 2026-10-21. /record/TRV-2026-2058 (accessed at citation time). sha256 df093f74642342ad…
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