Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.

Background: Childhood obesity represents a significant global public health challenge. Accurate and rapid prediction models for identifying obesity risk in children and adolescents are essential for facilitating early prevention and enabling timely interventions. However, interpretable and user-friendly obesity risk prediction models based on nationally representative data remain limited. This study aimed to develop and validate a model to predict current obesity risk among children and adolescents in China. Methods: The models were developed using cross-sectional data from the 2017-2018 Physical Activity and Fitness in China-The Youth Study (PAFCTYS; Results: The random forest (RF) model outperformed all other models, achieving an AUC of 1.000 on the training set and 0.946 on the testing set. It also maintained good discriminative performance on the temporal validation dataset, with an AUC of 0.810. The RF model also shows the highest accuracy, specificity, and MCC, and the lowest Brier score. SHAP analysis indicates that parental BMI, using mobile electronic devices on weekdays, MVPA (moderate-to-vigorous physical activity), watching TV on weekdays, and sex are the top 5 most important features in the obesity risk prediction model. A web-based risk calculator based on the RF model has been successfully deployed. Conclusion: Using a large, nationally representative sample and readily available variables, we successfully developed and validated an obesity risk prediction model for Chinese children and adolescents using explainable machine learning techniques. The model can quickly and accurately assess an individual's current obesity risk, enabling healthcare agencies, schools, and parents to conduct large-scale obesity risk screening.