• Computer Science > Artificial Intelligence [Submitted on 23 Feb 2026] Title:Multilevel Determinants of Overweight and Obesity Among U.S. • Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children’s Health View PDF HTML (experimental)Abstract:Background: Childhood and adolescent overweight and obesity remain major public health concerns in the United States and are shaped by behavioral, household, and community factors. • Their joint predictive structure at the population level remains incompletely characterized. • Objectives: The study aims to identify multilevel predictors of overweight and obesity among U.S. • adolescents and compare the predictive performance, calibration, and subgroup equity of statistical, machine-learning, and deep-learning models. • Data and Methods: We analyze 18,792 children aged 10-17 years from the 2021 National Survey of Children’s Health.

Article Summaries:

  • A recent study examined factors linked to overweight and obesity in U.S. adolescents (ages 10‑17) using 18,792 participants from the 2021 National Survey of Children’s Health. Researchers compared traditional logistic regression with several machine‑learning and deep‑learning models (random forest, gradient boosting, XGBoost, LightGBM, multilayer perceptron, TabNet). Predictors included diet, activity, sleep, parental stress, socioeconomic status, adverse experiences, and neighborhood characteristics. Models achieved AUCs between 0.66 and 0.79; logistic regression, gradient boosting, and MLP offered the best balance of discrimination and calibration, while boosting and deep‑learning slightly improved recall and F1. No algorithm outperformed all others, and disparities across race and poverty groups remained, underscoring the need for better data quality and equity‑focused surveillance rather than added model complexity.

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