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

Dohyun Chun1,2, Taesung Chung3, Jongho Kang2,4

  • 1College of Business Administration, Kangwon National University, Chuncheon, Gangwon-do, Korea.

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|March 31, 2025
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Summary

Body composition, including soft lean mass and body fat mass percentage, accurately predicts height in children and adolescents. This AI model offers interpretable insights for pediatric growth assessment.

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Body composition big datachildren and adolescentsexplainable AIheight estimationmachine-learning

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Area of Science:

  • Pediatric endocrinology
  • Biostatistics
  • Artificial Intelligence in Medicine

Background:

  • Accurate height estimation is crucial for monitoring child and adolescent growth and identifying potential health issues.
  • Traditional methods may not fully capture the complex interplay of factors influencing growth.
  • Leveraging advanced AI and body composition data can enhance predictive accuracy and interpretability.

Purpose of the Study:

  • To develop a precise and interpretable height estimation model for pediatric populations.
  • To utilize body composition variables and explainable AI (XAI) techniques for height prediction.
  • To identify key body composition indicators that influence height in children and adolescents.

Main Methods:

  • A light gradient boosting machine learning model was trained on a large dataset (n=54,374) of children and adolescents (ages 6-18).
  • The model incorporated anthropometric and detailed body composition measures.
  • Explainable AI methods, including SHAP and PDP, were used to interpret model predictions.

Main Results:

  • The height estimation model demonstrated high accuracy, with mean absolute percentage errors of 1.64% for boys and 1.63% for girls.
  • Soft lean mass (SLM) and body fat mass percentage (BFMP) were identified as significant predictors of height.
  • A positive association was observed between SLM and estimated height, whereas BFMP showed an inverse relationship.

Conclusions:

  • Body composition variables are reliable predictors of height in pediatric populations.
  • The developed AI model provides accurate and interpretable insights into height estimation.
  • This approach holds promise for advancing pediatric growth assessment and monitoring tools.