AI Driven Predictive System for PCOS Detection and Management

This project, titled "AI-Driven Predictive System for PCOS Detection and Management," focuses on developing a machine learning-based model to predict the likelihood of Polycystic Ovary Syndrome (PCOS) in individuals using medical and lifestyle data. The system uses algorithms such as Support Vector Machine (SVM), Random Forest, Stacking Classifier and XGBoost to analyze key features like age, weight, hormone levels, and menstrual cycle details to predict whether an individual is at high or low risk for PCOS. The goal is to provide a quick, cost-effective, and accurate tool to assist healthcare professionals and individuals in identifying PCOS early, enabling timely interventions and improved management. The system is designed to be user-friendly, with a web-based interface that allows users to input their data and receive predictions easily.

Keywords: PCOS, Machine Learning, Predictive System, SVM, Random Forest, Stacking Classifier, XGBoost, Data Preprocessing, Early Detection, Healthcare Technology.