External 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-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. Results: We retained 5 features per the Chinese growth standards and 12 per the WHO growth standards. Age, maternal body mass index (BMI), paternal BMI, screen time, and birthweight were consistent predictors. The neural network achieved optimal performance per Chinese growth standards (area under the receiver operating characteristic curve (AUROC) = 0.735), and eXtreme gradient boosting performed best per WHO growth standards (AUROC = 0.736), with the corresponding external validation AUROCs of 0.699 and 0.707. We subsequently deployed user-friendly online prediction tools for translation. Conclusions: We identified two interpretable ML models for obesity in children and adolescents per both Chinese and WHO growth standards, each demonstrating modest but reliable predictive performance.