Height estimation in children and adolescents using body composition big data: Machine-learning and explainable artificial intelligence approach.

ABSTRACT:

Objective

To develop an accurate and interpretable height estimation model for children and adolescents using body composition variables and explainable artificial intelligence approaches.

Methods

A light gradient boosting method was employed on a dataset of 278,301 measurements from 54,374 children and adolescents aged 6-18 years. The model incorporated anthropometric and body composition measures. Model interpretability was enhanced through feature importance analysis, Shapley additive explanations, partial dependence plots, and accumulated local effects.

Results

The models achieved high accuracy with mean absolute percentage errors of 1.64% and 1.63% for boys and girls, respectively. Soft lean mass (SLM), body fat mass percentage (BFMP), skeletal muscle mass, and s

SUBMITTER: Chun D 

PROVIDER: S-EPMC11951887 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature