Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.

ABSTRACT:

Background

Accurate antenatal prediction of SGA at birth is essential to improve development and delivery of preventative and therapeutic interventions. This study aimed to assess the performance of machine learning (ML) models to predict SGA at birth among Chinese pregnancies classified according to the Chinese birthweight standard and three international birthweight standards.

Methods

We collected multimodal, longitudinal, antenatal surveillance data on 350,135 singleton pregnancies in Wenzhou City, China, between Jan 1, 2014 and Dec 31, 2016. For three pregnancy intervals we developed ML prediction models for newborns classified as SGA using the China, Intergrowth 21st, Fetal Medicine Foundation (FMF), and Gestation-related Optimal Weight (GROW) standards. We applied lass

SUBMITTER: Yu QY 

PROVIDER: S-EPMC12832878 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature