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

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.

Methods

Data from 84,050 mother-child pairs, collected in 2021 and 2022, were processed and divided into training, validation, and test sets. Six ML models were tested: L1-Regularised Logistic Regression, Light Grad

SUBMITTER: Ding L 

PROVIDER: S-EPMC11616287 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature