Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review

Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after first 20 weeks pregnancy marked by proteinuria hypertension. It can affect pregnant women limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% pregnancies worldwide are affected hypertensive disorders during pregnancy. In this review, we discuss machine learning deep methods preeclampsia prediction that were published between 2018 2022. Many models have been created using variety data types, including demographic clinical data. We determined techniques successfully predicted preeclampsia. The used most random forest, support vector machine, artificial neural network (ANN). addition, prospects challenges discussed to boost research on intelligence systems, allowing academics practitioners improve their advance automated prediction.