Prediction of Preeclampsia Using Machine Learning and Deep Learning Models: A Review
Sumayh S. Aljameel, Manar Alzahrani, Reem Almusharraf, Majd Altukhais, Sadeem Alshaia, Hanan Sahlouli, Nida Aslam, Irfan Ullah Khan, Dina A. Alabbad, Albandari Alsumayt
Abstract
Preeclampsia is one of the illnesses associated with placental dysfunction and pregnancy-induced hypertension, which appears after the first 20 weeks of pregnancy and is marked by proteinuria and hypertension. It can affect pregnant women and limit fetal growth, resulting in low birth weights, a risk factor for neonatal mortality. Approximately 10% of pregnancies worldwide are affected by hypertensive disorders during pregnancy. In this review, we discuss the machine learning and deep learning methods for preeclampsia prediction that were published between 2018 and 2022. Many models have been created using a variety of data types, including demographic and clinical data. We determined the techniques that successfully predicted preeclampsia. The methods that were used the most are random forest, support vector machine, and artificial neural network (ANN). In addition, the prospects and challenges in preeclampsia prediction are discussed to boost the research on artificial intelligence systems, allowing academics and practitioners to improve their methods and advance automated prediction.