Prediction of spontaneous preterm birth in pregnant women using machine learning.

ABSTRACT:

Purpose

Spontaneous preterm birth (sPTB) is a significant global health concern, contributing to adverse outcomes for both pregnant women and newborns. Early identification of women with risk of sPTB is essential for mitigating these negative effects and improving maternal and neonatal health outcomes. The aim of this study is to explore the feasibility of using machine learning to predict sPTB risk and to analyze the contribution of variables.

Methods

All data were collected retrospectively. Prediction models were developed using eight different machine learning algorithms combined with six variable selection methods. The models' predictive performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (

SUBMITTER: Yang X 

PROVIDER: S-EPMC12414023 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature