Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.

ABSTRACT: Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass index across the full pediatric spectrum. Additionally, they consider only the immediate neighborhood within which a child lives, ignoring contextual factors from expanded (i.e., distal) neighborhoods. This study uses expanded neighborhoods' social, environmental, and climate data to improve individual-level body mass index prediction-from underweight through obesity-using a novel machine learning approach. We obtained demographic and clinical data from the electronic health records of the Duke University Health System, identifying 12,226 ch

SUBMITTER: Li K 

PROVIDER: S-EPMC12539641 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature