INTRODUCTION TO POLYCYSTIC OVARY SYNDROME DETECTION MACHINE LEARNING MODEL BASED ON OPTIMIZED FEATURE SELECTION

Computer Engineering

This paper introduces an innovative approach to Polycystic Ovary Syndrome (PCOS) detection through the development of a web-based application. PCOS is a prevalent and complex endocrine disorder affecting millions of individuals worldwide. Our solution leverages modern tools, including Streamlit, for effective web app development. It incorporates machine learning algorithms to enable early and accurate PCOS prediction based on user-provided data and genetic features.This project encompasses data collection, preprocessing, and model training, employing Bayesian optimization and stacked deep ensemble learning techniques. By combining these advanced methodologies, our web application can enhance the prediction of PCOS, assisting individuals in early diagnosis and treatment. This novel tool's effectiveness is evaluated using a diverse dataset, showcasing its potential to revolutionize PCOS diagnosis and healthcare accessibility.The study demonstrates the power of modern technology, particularly Streamlit, in developing user-friendly and efficient healthcare tools. It highlights the significance of early PCOS detection and the role of machine learning in advancing medical diagnostics. The application's potential impact on improving healthcare outcomes and reducing PCOS-related health costs is substantial, signifying a promising endeavor at the intersection of healthcare and technology.

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