Antepartum prediction of shoulder dystocia using machine learning.

ABSTRACT:

Purpose

Shoulder dystocia (SD) is a serious delivery complication, often occurring without identifiable risk factors. Accurate risk assessment is essential for educated decisions regarding the mode of delivery. This study aimed to develop and validate a machine learning-based model for SD prediction.

Methods

We conducted a retrospective analysis of term singleton vaginal deliveries at a single academic hospital over 10 years. Exclusion criteria included cesarean deliveries and multiple gestations. Maternal and fetal characteristics were compared between SD and non-SD cases. Eleven features were selected for model development. Missing values in normally distributed variables were imputed using mean values. To address class imbalance, repeated random sampling of non-SD cases w

SUBMITTER: Heresco L 

PROVIDER: S-EPMC12756293 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature