Novel maternal risk factors for preeclampsia prediction using machine learning algorithms

Abstract

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Preeclampsia and eclampsia are the most common obstetric disorders associated with poor outcomes for women, fetuses, and newborns throughout the perinatal period. The study’s primary objective was to assess the accuracy of novel high-risk factors using MLA in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including DT, RF, Gradient Boosting, SVM, K-Neighbors, LGBM, MLP, Ada Boost Classifier, and Extra Trees Classifier are used in the analysis. The accuracy and precision of the LGBM Classifier (0.85 and 0.9583 with F1 0.7188), Support Vector Classifier (0.8417 and 0.92 with F1 0.7077), Decision Tree (0.825 and 0.913 with F1 0.6667), and Extra Trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia than those of the other models used in the study. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a BP of 140/90 mmHg and the above. Novel, high-risk scores and MLA can effectively predict preeclampsia at an early period.</p></td></tr></tbody></table></div>