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Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple

Hongjun Tao1, Yang Wen2, Rongfang Yu3

  • 1Department of Physical and Education, Anhui Jianzhu University, Hefei, China.

Frontiers in Bioengineering and Biotechnology
|June 16, 2025
PubMed
Summary
This summary is machine-generated.

Forward head posture in children can be predicted using machine learning. Key indicators include age, body mass index (BMI), and bodyweight, enabling early intervention.

Keywords:
forward head posturemachine learningprimary school-aged childrenrisk prediction modelshapley additive explanation algorithm

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Area of Science:

  • Pediatrics
  • Biostatistics
  • Computer Science

Background:

  • Forward head posture is prevalent in primary school children, often linked to sedentary behavior and academic pressures.
  • Current screening methods lack accuracy and promptness in predicting this condition.

Purpose of the Study:

  • To identify sensitive predictive indicators for forward head posture in primary school children.
  • To develop and compare machine learning models for risk prediction using LASSO regression and SHAP analysis.

Main Methods:

  • A cross-sectional study included 514 primary school children.
  • LASSO regression identified risk factors; six machine learning models (KNN, LGBM, XGBoost, RF, LM, SVM) were built.
  • The Random Forest model was selected for its superior performance and interpreted using SHAP.

Main Results:

  • Age, bodyweight, BMI, sex, and homework time were identified as significant risk indicators by LASSO.
  • The Random Forest model achieved the highest predictive accuracy (AUC = 0.865).
  • SHAP analysis highlighted BMI, bodyweight, and age as the most influential predictors, with BMI being the primary factor.

Conclusions:

  • A Random Forest-based model demonstrates high predictive accuracy for forward head posture in Chinese primary school children.
  • Monitoring BMI, bodyweight, and age is crucial for early detection and prevention strategies.