Predicting metabolic syndrome in adolescents from device-measured 24-h movement behaviors: a machine learning...

To develop and internally evaluate a non-invasive machine-learning model for identifying metabolic syndrome (MS) in adolescents using 24-h movement behaviors and non-invasively measured physical characteristics.

This cross-sectional study included 822 adolescents aged 12-15 years recruited in Tianjin, China, from September 2023 to September 2025. Movement behaviors were assessed using ActiGraph GT3X+ accelerometers, body composition was measured using dual-energy X-ray absorptiometry, and MS was diagnosed according to the 2007 International Diabetes Federation criteria. LASSO regression was used for feature selection, and 13 machine-learning algorithms were compared. SMOTETomek was used during model development to address class imbalance. Performance was evaluated using stratified five-fold cross-validation and a held-out internal test set. SHapley Additive exPlanations (SHAP) were used to interpret model predictions.

LASSO retained eight predictors: waist circumference, body fat percentage (FAT%), weight, age, moderate physical activity (MPA), vigorous physical activity, sleep duration, and sex. Extra Trees achieved the best cross-validation performance, with a mean AUC of 0.749 (SD, 0.043) and an F1 score of 0.798 (SD, 0.028). Its relative advantage may reflect the ability of randomized tree ensembles to capture nonlinear relationships and feature interactions while reducing variance through aggregation. In the held-out internal test set, the model achieved an AUC of 0.864, accuracy of 92.02%, specificity of 93.33%, and sensitivity of 76.92%. The lower sensitivity relative to accuracy and specificity indicates that some adolescents with MS may remain undetected. SHAP ranked FAT% as the most influential predictor (mean absolute SHAP value, 0.08) and MPA as the leading movement-behavior predictor (0.04), thereby improving the transparency of model predictions.

The Extra Trees model showed moderate cross-validation discrimination and stronger performance in the internal test set, although this difference warrants cautious interpretation. SHAP improved model transparency by identifying FAT% and MPA as influential predictors. Multicenter external validation and feasibility assessment are required before large-scale screening.