Predicting stillbirth and identifying key maternal risk factors using machine learning.

ABSTRACT:

Background

Stillbirth remains a major public health concern, particularly in low-income and middle-income countries. Identifying maternal and obstetric determinants is essential for prevention and targeted interventions. Logistic regression offers a baseline predictive model, while machine learning (ML) methods, such as Random Forest (RF) and Extreme Gradient Boosting, can improve predictive accuracy and highlight key risk factors through feature importance. This study aimed to predict stillbirth and identify influential maternal and obstetric predictors among pregnant women in Ethiopia using ML models.

Methods

A retrospective cross-sectional study was conducted using maternal and obstetric records from Bishoftu General Hospital, Ethiopia. Predictors included maternal age, w

SUBMITTER: Aga MA 

PROVIDER: S-EPMC12574342 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature