A Machine Learning and Evaluation Framework for Analyzing the Impact of Maternal Health on Infant Development

Summary

This study establishes machine learning models to analyze correlations between infant behavior characteristics, maternal physical and mental health indicators, and infant sleep quality, and proposes treatment strategies.

Problem 1

  • Preprocessed infant behavior features and maternal health indicators.
  • Designed hierarchical statistics for multiple variables: ANOVA for continuous variables and logistic regression for categorical variables.
  • Conducted correlation analysis and multifactor ANOVA, finding significant relationships:
    • Maternal age ↔ infant sleep patterns & behavior features
    • Maternal gestation period ↔ infant wake-up frequency
    • Maternal HADS score ↔ infant wake-up frequency, total sleep time, behavior features
    • Maternal EPDS score ↔ infant wake-up frequency, total sleep time
  • No significant effects were found for other indicators.

Problem 2

  • Trained models using logistic regression, Random Forest, Neural Networks, and XGBoost.
  • Selected XGBoost as the best-performing model (highest accuracy).
  • Optimized model parameters using loss function minimization and cross-validation.
  • Predicted the behavior types for the last 20 infant samples using the trained XGBoost model.

Problem 3

  • Combined genetic algorithms with the XGBoost model from Problem 2 to generate treatment plans.
  • Final treatment costs:
    • Moderate type: 695 CNY
    • Quiet type: 10,448 CNY

Problem 4

  • Evaluated infant sleep quality using the CRITIC method.
  • Established a comprehensive sleep quality ranking system using rank-sum ratio evaluation, classifying sleep quality as excellent, good, medium, or poor.
  • Determined indicator weights with the CRITIC method.
  • Trained a Random Forest model to associate comprehensive infant sleep quality with maternal health indicators, predicting sleep quality for the last 20 infant samples.

Problem 5

  • Based on the evaluation and association models from Problem 4, calculated the initial sleep quality of infant #238.
  • Applied the same approach as Problem 3, updating maternal indicators in the association model to generate a new treatment plan:
    • Moderate type (sleep quality: excellent), minimum cost: 8,699 CNY

Keywords: XGBoost, Genetic Algorithm, CRITIC Method, Rank-Sum Ratio Evaluation, Random Forest, Association Model

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A Machine Learning and Evaluation Framework for Analyzing the Impact of Maternal Health on Infant Development