
ML vs Stats for Child Obesity Prediction
Researchers compared statistical, ML, and deep learning models on 18,792 US children aged 10-17 from 2021 NSCH to predict overweight/obesity. AUC ranged 0.66-0.79, with logistic regression, gradient boosting, and MLP offering the best balance of discrimination and calibration. Complex models provided limited gains, and subgroup disparities persisted across races and poverty levels.



