Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.

ABSTRACT:

Introduction

Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + ⁶ weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia.

Methods

This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in Chin

SUBMITTER: Li T 

PROVIDER: S-EPMC11198873 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature