Journal of global health
External validation and application of machine learning models for obesity in Chinese children and adolescents per Chinese and WHO growth standards
By Zheng F, Wang X, Xue M and 4 others.
Abstract
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-sectional study. We used data from 22,555 participants in Beijing (2022, 2024) for training/internal validation, and data from 5,343 participants in Tangshan (2020) for external validation. We defined obesity using both Chinese and WHO growth standards. We analysed 38 features across 5 domains; we selected key predictors using least absolute shrinkage and selection operator regression. We evaluated 4 ML models using 28 performance metrics, with SHapley Additive exPlanations enhancing interpretability.