Polycystic Ovary Syndrome Risk Detection Using Machine Learning

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder affecting women of reproductive age, causing issues such as irregular menstrual cycles, weight gain, and infertility, making early detection very important for proper treatment and management. Traditional diagnostic methods are often time-consuming and require multiple medical tests, which can delay diagnosis. The aim of this project is to develop an intelligent system using machine learning techniques to detect PCOS at an early stage with better accuracy. This system uses patient data such as age, body mass index (BMI), hormonal levels, and other clinical parameters for prediction. The objective of the project is to collect and preprocess the dataset, apply different machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest, and compare their performance based on evaluation metrics like accuracy, precision, and recall. The system analyzes the input data and predicts whether a patient is likely to have PCOS or not. By comparing multiple algorithms, the most efficient model is selected for better prediction results. This approach helps in reducing manual effort and supports healthcare professionals in making faster and more reliable decisions. The proposed system is cost-effective, user-friendly, and can be used as a supportive diagnostic tool. Overall, the project demonstrates that machine learning can play a significant role in improving early detection and management of PCOS.