Height estimation in children and adolescents using body composition big data: Machine-learning and explainable artificial intelligence approach - Dohyun Chun; Taesung Chung; Jongho Kang; Taehoon Ko; Young-Jun Rhie; Jihun Kim (Digital Health)
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 skeletal muscle mass percentage were consistently identified as key factors influencing height estimation. Analysis revealed a positive correlation between SLM and estimated height, while BFMP exhibited an inverse relationship with height projections. Conclusion These findings provide valuable insights into the relationship between body composition and height, underlining the potential of body composition variables as accurate height predictors in children and adolescents. The model's interpretability and accuracy make it a promising tool for pediatric growth assessment and monitoring.