Machine learning-based management of hypertensive disorders in pregnancy: analysis of differences in key risk factors between gestational hypertension and pre-eclampsia and construction of a pre-eclampsia prediction model.

ABSTRACT:

Objectives

It remains debated whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectra of the same disease. Currently, comparative studies of risk factors for GH and PE in the same population are limited. This study aims to identify the key differential risk factors between GH and PE, and develop a machine learning (ML)-based PE prediction model to address the clinical needs of low-resource settings.

Methods

A single-center retrospective analysis included 1,157 pregnant women who established prenatal care records and delivered at Hangzhou Women's Hospital between 2019 and 2022. Five ML models-adaptive boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression (LR), and gradient boosting (GB

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12625445 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature