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PCOS Detection using DeepLearning

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First-ever all in one app for women diagnosed with PCOS!

A project dedicated to PCOS , which is so common diseases yet not know. September is the PCOS awareness month and with this project I tried to create some awareness.

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

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

This repository contains all material related to the project done as a part of the course Algorithmic Approaches to Computational Biology (CS6024) in the Fall 2020 semester.

Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact etiology of the disease. The exact histology of PCOS is not yet clear. It is therefore a multifaceted study, which shares genetic and environmental factors. The aim of this project is to analyse simple factors (height, weight, lifestyle changes, etc.) and complex (imbalances of bio hormones and chemicals such as insulin, vitamin D, etc.) factors that contribute to the development of the disease. The data we used for our project was published in Kaggle, written by Prasoon Kottarathil, called Polycystic ovary syndrome (PCOS) in 2020. This database contains records of 543 PCOS patients tested on the basis of 40 parameters. For this, we have used Machine Learning techniques such as Logistic Regression, Decision Trees, SVMs, Random Forests, etc, A detailed analysis of all the items made using graphs and programs and prediction using Machine Learning Models helped us to identify the most important indicators for the same.

Harmony Hormones is an AI-powered platform focused on women's health and well-being. It offers a personalized period calendar, care routines through Flow AI, and expert menstrual health guidance with Maitri AI, powered by CopilotKit.

PCOS Prediction API

Explored and compared different algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and Naive Bayes for the prediction of PCOS

Identification for Key Pathways and Genes in Polycystic Ovary Syndrome (PCOS) using a multi-omics approach, as part of the Applied High Throughput Analysis course at Ghent University

This project builds a PCOS (Polycystic Ovary Syndrome) prediction model using inexpensive clinical and non-clinical symptoms from a medical dataset. The model applies Logistic Regression to analyze correlations between key health indicators and predict whether a patient has PCOS.

PCOS Subtype Discovery & Clustering Robustness Analysis - Reproduces Nature Medicine (2025) research on identifying clinically distinct PCOS subtypes using unsupervised learning

Online Poll Survey and Appointment

A project dedicated to PCOS , which is so common diseases yet not know. September is the PCOS awareness month and with this project I tried to create some awareness.

Reproducible WGCNA pipeline for PCOS microarray dataset GSE48301

Final Year Project — PCOS & Insulin Resistance wellness app (React Native + Expo + Firebase). Track cycles, meals & symptoms, get insights, gentle nudges, and pro content.

A website that predicts PCOS based on optimal and minimal clinical and metabolic parameters. The dataset is obtained from kaggle which is a patient survey of 541 women during consultation and clinical examination.

Engineered Deep Neural Network models for automated disease detection from medical dataset of PCOS images, outperforming pre-trained models.

A PowerBI dashboard styled as an infographic. A 2023 case study of PCOS (Polycystic Ovary Syndrome) and it's symptoms.

Data analytics project on PCOS using Python, Excel, and Tableau | QHO539 Solent University

Exploring Predictive Health Factors

This project identifies patterns in symptom presentation among patients with and without PCOS. It uses medical datasets, Python-based data cleaning and transformation, and network analysis to visualize co-occurrence relationships.

Code for Context-specific metabolic network model for PCOS with insulin resistance

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.

Mengelola PCOS dengan cara yang sehat. Website ini menyediakan informasi dan tips gaya hidup, nutrisi, dan manajemen stres untuk membantu Anda mengatasi gejala PCOS dan meningkatkan kualitas hidup secara menyeluruh. Mulai perjalanan sehat Anda hari ini.

AI-driven Polycystic Ovary Syndrome (PCOS) health app using fuzzy logic to analyze symptoms, predict risks, and generate insights. Features cycle tracking, dashboards, and personalized recommendations. Built with React, Node.js

Algorithmic analysis on PolyCystic Ovarian Syndrome data