Proposed model to predict preeclampsia using machine learning approach

Abstract

Pregnancy complications, which are the biggest cause of death in productive women, are more common in developing countries with low incomes. One of the contributors to death in pregnant women is preeclampsia which contributes 2-8% every day. Based on research results, more than 70% of the use of technology can be a solution for early prevention in detecting cases of pregnancy. The aim of this research is to build a model for early detection of preeclampsia using a machine learning approach. Sample using retrospective data with sample size 1.473. Based on the result, decision tree (DT) is the best model with accuracy 92.2% (area under curve (AUC): 0.91; Spec: 92.3; and Sens: 83.6), according to weigh correlation we can show 3 (three) highest features causes preeclampsia is history of hypertension, history of diabetes mellitus, and history of preeclampsia. The health of pregnant women is essential in the development of the fetus, so it needs optimal monitoring. Monitoring during pregnancy can now be done through technology-based examinations for assist health workers in making decisions during pregnancy.