pcos-detection

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

A unified centralized and federated learning benchmark for PCOS diagnosis with Explainable AI (SHAP) across multi-clinic distributions, evaluating clinical attribution consistency between centralized models and privacy-preserving federated ensembles

Multi-modal AI system for PCOS detection using lifestyle data, ultrasound images, and RAG-based clinical recommendations.

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.

Early detection of PCOS using a hybrid BiLSTM-GRU-CNN model with feature selection, data augmentation (ADASYN), and explainable AI techniques.

live project

AI-assisted PCOS detection using CNN and Streamlit for ultrasound image analysis and lifestyle recommendations.

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.

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.

End-to-end Machine Learning project for PCOS prediction including preprocessing, scaling, normalization, model training (Random Forest, Logistic Regression, SVM, KNN), evaluation, visualization, and real-time prediction.

Production-grade clinical AI for PCOS detection calibrated ensemble (AUROC 0.94), SHAP explainability, FastAPI + Next.js, fully deployed on free-tier infrastructure.

Statistical and machine-learning analysis of dietary, exercise, and health factors associated with PCOS.