Artificial intelligence for pediatric height prediction using large-scale longitudinal body composition data.

ABSTRACT:

Objective

We developed a precise, reliable artificial intelligence (AI) model for predicting the future height of children and adolescents based on anthropometric and body composition data.

Materials and methods

We used an extensive longitudinal dataset from a large-scale Korean cohort study, which included 588,546 measurements from 96,485 children and adolescents aged 7-18. We developed a prediction model using the light gradient boosting method and integrated anthropometric and body composition metrics along with their standard deviation scores (SDSs) and velocity parameters. Model performance was assessed through root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). We employed Shapley additive explanations (SHAP) for model i

SUBMITTER: Chun D 

PROVIDER: S-EPMC12644449 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature