Pediatric obesity · 2026
Machine Learning Prediction of Obesity Development in Children With Overweight Using Longitudinal Body Composition Data.
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Chun Dohyun, Rhie Young-Jun, Sawyer Jason, Kang Jongho, Yoon Nathan, Kim Jihun
Аннотация
BACKGROUND: Children with overweight (body mass index [BMI] between the 85th and 95th percentiles) represent a critical target for obesity prevention, with obesity progression rates 10- to 20-fold higher than normal-weight peers.
OBJECTIVES: This study developed machine learning models to predict obesity in this high-risk group using anthropometric and bioelectrical impedance analysis parameters.
METHODS: We analysed longitudinal data from 2801 overweight Korean children aged 7-9 years. XGBoost models integrated anthropometric measurements, bioelectrical impedance-derived body composition, standardised deviation scores and growth velocity parameters to predict obesity (BMI ≥ 95th percentile). Sex-stratified models were evaluated using area under the receiver operating characteristic curve (AUROC) with bootstrap validation. Shapley Additive exPlanations (SHAP) identified key predictive features.
RESULTS: Obesity developed in 32.3% of males and 25.4% of females during follow-up. Models achieved AUROC scores of 0.671 (95% CI: 0.619-0.721) for males and 0.652 (95% CI: 0.589-0.700) for females. Key predictors included standardised weight and adiposity measures, height-adjusted skeletal muscle mass and growth velocity parameters.
CONCLUSIONS: Machine learning models demonstrated effective predictive performance for obesity in children with overweight. Incorporating standardised adiposity measures and growth velocity beyond static anthropometric data provides a robust framework for risk stratification in this high-risk population.
OBJECTIVES: This study developed machine learning models to predict obesity in this high-risk group using anthropometric and bioelectrical impedance analysis parameters.
METHODS: We analysed longitudinal data from 2801 overweight Korean children aged 7-9 years. XGBoost models integrated anthropometric measurements, bioelectrical impedance-derived body composition, standardised deviation scores and growth velocity parameters to predict obesity (BMI ≥ 95th percentile). Sex-stratified models were evaluated using area under the receiver operating characteristic curve (AUROC) with bootstrap validation. Shapley Additive exPlanations (SHAP) identified key predictive features.
RESULTS: Obesity developed in 32.3% of males and 25.4% of females during follow-up. Models achieved AUROC scores of 0.671 (95% CI: 0.619-0.721) for males and 0.652 (95% CI: 0.589-0.700) for females. Key predictors included standardised weight and adiposity measures, height-adjusted skeletal muscle mass and growth velocity parameters.
CONCLUSIONS: Machine learning models demonstrated effective predictive performance for obesity in children with overweight. Incorporating standardised adiposity measures and growth velocity beyond static anthropometric data provides a robust framework for risk stratification in this high-risk population.