Prediction Of Preeclampsia Using Machine Learning Model

In this project, I developed a machine learning model to predict the onset of pre-eclampsia in pregnant women, leveraging patient data to enhance early diagnosis and intervention. Utilizing a dataset comprising clinical and demographic information, we implemented various algorithms, including logistic regression, random forests, and support vector machines, to identify the most accurate predictive model. The model was trained and validated using robust statistical techniques to ensure high accuracy and reliability. This predictive tool aims to assist healthcare providers in identifying high-risk pregnancies early, thereby improving maternal and fetal health outcomes through timely medical intervention.