Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.

Journal: BMC pregnancy and childbirth
PMID: 39633287

Abstract

BACKGROUND: Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive methods face challenges due to heterogeneous risk factors and their interaction effects. This study aims to develop and evaluate six machine learning (ML) models to predict PTB using large-scale children survey data from Shenzhen, China, and to identify key predictors through Shapley Additive Explanations (SHAP) analysis.

Authors

  • Liwen Ding
    Department of Epidemiology and Health Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Xiaona Yin
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China.
  • Guomin Wen
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China.
  • Dengli Sun
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China.
  • Danxia Xian
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China.
  • Yafen Zhao
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China.
  • Maolin Zhang
    Department of Epidemiology and Health Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
  • Weikang Yang
    Women's and Children's Hospital of Longhua District of Shenzhen, Shenzhen, 518109, China. yangweikang@lhfywork.com.
  • Weiqing Chen
    Department of Epidemiology and Health Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. chenwq@mail.sysu.edu.cn.

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