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Research on children's health prediction based on Improved Grey Wolf Optimization algorithm-Random Forest model

Huan Xu1, Junying Hu2

  • 1Department of Public Teaching, Hefei Preschool Education College, Hefei, China.

Medicine
|February 3, 2026
PubMed

Insights

A new hybrid model (IGWO-RF) improves pediatric health prediction accuracy to 92.1% by optimizing Random Forest hyperparameters. Key health determinants include BMI, exercise, and PM2.5 exposure, offering potential for early risk stratification.

Area of Science:

  • Pediatric Health
  • Computational Health
  • Artificial Intelligence in Medicine

Background:

  • Childhood health is vital for public health assessment, yet faces challenges from lifestyle changes and environmental factors, leading to increased obesity, allergies, and respiratory issues.
  • Traditional health assessments suffer from data lag and subjectivity, necessitating advanced predictive models.
  • The complexity of pediatric health necessitates innovative approaches for accurate and timely risk assessment.

Purpose of the Study:

  • To introduce a novel hybrid model, Improved Grey Wolf Optimization-Random Forest (IGWO-RF), for enhanced pediatric health prediction.
  • To improve the accuracy and interpretability of health prediction models using children's physical examination data.
  • To identify key determinants of children's health through advanced explainable AI techniques.

Main Methods:

  • A Random Forest (RF) model was developed using children's physical examination data.
  • The Grey Wolf Optimization (GWO) algorithm was enhanced with dynamic weight strategies and elite retention mechanisms (IGWO) to optimize RF hyperparameters.
  • Shapley Additive Explanations (SHAP) values were employed for model interpretability and identification of significant health factors.

Main Results:

  • The IGWO-RF model achieved a prediction accuracy of 92.1% and a F1-score of 90.8%, outperforming traditional RF (85.3%) and PSO-RF (88.7%).
  • SHAP analysis identified body mass index (0.32), daily exercise time (0.21), and particulate matter 2.5 exposure (0.18) as the primary determinants of children's health.
  • The model demonstrated superior performance in pediatric health risk stratification.

Conclusions:

  • The IGWO-RF model offers a significant advancement in pediatric health prediction accuracy and interpretability.
  • Key factors influencing children's health, such as BMI, exercise, and environmental exposures, were quantitatively identified.
  • The proposed methodological framework shows promise for developing early warning systems for pediatric health risks and other chronic diseases.

Keywords:
Grey Wolf OptimizationRandom Forestchildren’s health predictionhealthcare analyticsmachine learning

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