Machine learning predictive system to predict the risk of developing pre-eclampsia.

ABSTRACT:

Objectives

To develop a machine learning (ML)-based predictive model for assessing the risk of pre-eclampsia using routinely collected clinical data.

Methods

We retrospectively analysed data from 2444 pregnant women who delivered at Chi Mei Medical Center between 2015 and 2019, excluding patients under 20 years old. Pre-eclampsia was defined as blood pressure >140/90 mm Hg with proteinuria. Key features included gestational age, body weight, blood pressure and medical history. Five ML models were trained-logistic regression, random forest, light gradient boosting machine, extreme gradient boosting (XGBoost) and multilayer perceptron-using a 70/30 train-validation split. Synthetic minority oversampling technique was applied to address class imbalance. SHapley Additive exPlana

SUBMITTER: Shyu IL 

PROVIDER: S-EPMC12542561 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature