Pre-Eclampsia Predict: AI-Driven Early Detection System for Pregnant Women

Preeclampsia which is part of hypertensive renal syndrome presents in 5-8 percent of pregnant women worldwide to cause severe health risks for expecting mothers together with their unborn children. Doctors need urgent preeclampsia diagnosis since standard diagnostic methods give doubtful results which have unpredictable outcomes at the same time. Healthcare professionals gain recent clinical guidance through a Flask web interface that enables diagnosis of preeclampsia risk classifications. The analysis of 243 patient records revealed exceptional results from AdaBoost as it demonstrated <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 8 2 \%}$</tex> accuracy along with 0.988 F1-score across 69 clinical indicators. The operational procedure mandates healthcare providers to perform blood pressure tests followed by platelet count evaluation as the initial step then proceed with proteinuria assessment and HELLP syndrome marker inspection to identify medical risks starting with Low level risks before advancing to Severe risks. The system provides access to all features through its Flask interface and it increases performance reliability using Selenium automation tests. The system yields three main advantages from multiclass risk organization and its union with automated testing features alongside automated MAP evaluation systems. Multiple research reports demonstrate that technological progression delivers better user interactions to areas with minimal resources. ML-based systems benefit prenatal development through early detection of risks thereby helping worldwide mother survival by analyzing research data.