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Machine Learning Prediction of Obesity Development in Children With Overweight Using Longitudinal Body Composition

Dohyun Chun1,2, Young-Jun Rhie3, Jason Sawyer4

  • 1College of Business Administration, Kangwon National University, Chuncheon, Gangwon-do, Korea.

Pediatric Obesity
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning effectively predicts obesity in overweight children using anthropometric and body composition data. This approach aids early risk identification for better obesity prevention strategies in this vulnerable group.

Keywords:
body compositionchildren with overweightgrowth velocitymachine learningpaediatric obesity prediction

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Area of Science:

  • Pediatric Endocrinology
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Overweight children (BMI 85th-95th percentile) have a significantly higher risk of progressing to obesity.
  • Early identification and intervention are crucial for preventing long-term health consequences.

Purpose of the Study:

  • To develop and validate machine learning models for predicting obesity in overweight children.
  • To identify key predictive factors for obesity development in this high-risk group.

Main Methods:

  • Longitudinal data from 2801 overweight Korean children (7-9 years) were analyzed.
  • XGBoost models integrated anthropometric, bioelectrical impedance analysis (BIA)-derived body composition, and growth velocity parameters.
  • Sex-stratified models were evaluated using Area Under the Receiver Operating Characteristic Curve (AUROC) and Shapley Additive exPlanations (SHAP).

Main Results:

  • Obesity developed in 32.3% of males and 25.4% of females.
  • Models achieved AUROC scores of 0.671 for males and 0.652 for females.
  • Key predictors included standardized weight, adiposity measures, height-adjusted skeletal muscle mass, and growth velocity.

Conclusions:

  • Machine learning models show promise in predicting obesity risk in overweight children.
  • Integrating standardized adiposity and growth velocity data enhances predictive accuracy beyond static measurements.
  • This framework supports robust risk stratification for targeted obesity prevention.