
Preeclampsia is a serious pregnancy complication characterized by high blood pressure and organ dysfunction, which can pose significant risks to both the mother and the fetus, especially in resource-limited regions such as some areas in Algeria.
This work explores the potential of artificial intelligence to improve early screening of preeclampsia by developing accessible and effective medical tools.
We collected real data from 100 pregnant patients in the El Tarf region, then applied and compared three algorithms: Random Forest, Support Vector Machine (SVM), and Gaussian Mixture Model (GMM). The results were integrated into a simple graphical user interface (Tkinter) to facilitate clinical use by healthcare professionals.
Keywords: Preeclampsia, Artificial Intelligence, Random Forest, SVM, GMM