PCOS Detection using DeepLearning

a high-precision model as a cost-effective alternative for the early detection of PCOS, assisting medical professionals without relying on more invasive methods.

PCOS Prediction API

R project to diagnose PCOS using a decision tree algorithm. Use of machine learning models has scope to reduce healthcare costs, increase attention towards PCOS diagnosis and ultimately improve healthcare and quality of life for women.

MyOvae, a deeply personalized and AI-driven wellness companion designed to empower individuals navigating the complexities of Polycystic Ovary Syndrome (PCOS). This isn't just another tracking app; it's an intelligent guide that transforms personal health data into actionable, holistic insights.

This project evaluates various machine learning models for diagnosing Polycystic Ovary Syndrome (PCOS) based on medical and clinical features. It compares models like Decision Tree, XGBoost, Random Forest, SVM, and Logistic Regression, analyzing their accuracy and execution time to determine the best-performing model for PCOS prediction.

live project