Advancing Preeclampsia Prediction with Machine Learning: A Comprehensive Systematic Literature Review - Rahman , R. Topan-Aditya; Lakulu , Muhammad-Modi; Panessai , Ismail-Yusuf (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Preeclampsia is one of the leading causes of maternal mortality, which is a serious problem during pregnancy, which is further complicated by issues related to pathophysiology and etiology. The focus of this research is on the early detection of preeclampsia by using machine learning with multiple algorithms. Specifically, the aim of this study is to identify the causes of preeclampsia. A total of 21 articles were obtained from four scientific databases, namely ScienceDirect, Scopus, IEEE, and PubMed, which were published between 2018 and 2022, using several keywords such as “Artificial Intelligence”, “Machine Learning”, “Prediction”, and “Preeclampsia”. The method of the review adhered to the principles outlined in a guideline published by Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA). The systematic review of these articles was focused on the accuracy of prediction of preeclampsia using machine learning. The results showed machine learning was the most popular method, garnering 40.4% of mentions, followed by deep learning (11.5%), hybrid learning (2%), and other methods (19%), while other types of features, such as cell DNA, cohort, and resonance imaging received sizable mention (46.1%). Stochastic Gradient Boosting (SGBoost), which had an accuracy of 97.3%, was the most accurate algorithm. Machine learning is, therefore, deemed to be the best method for predicting pregnancy outcomes in light of these findings. Clearly, further research is needed to determine the best algorithm for developing prenatal diagnosis models, particularly for the early detection of preeclampsia.