PCOS Detection Using Machine Learning and Medical App Development

Polycystic Ovary Syndrome (PCOS) is a major health concern that affects fertility, metabolic health, and general quality of life. For effective management and the prevention of associated health problems, early detection and treatment of PCOS are crucial. This abstract describes a new method for PCOS identification that combines a specialized mobile application for PCOS with machine learning algorithms. The suggested system builds an accurate prediction model for PCOS detection by utilizing a wide range of characteristics, such as hormone levels, physical examination data, and medical history. Various machine learning techniques are employed to assess the collected data and classify individuals into PCOS-positive or PCOS- negative groups. The final output was predicted based on the most accurate machine learning algorithm. To make the detection process accessible and user-friendly, an application is developed. The PCOS app provides personalized recommendations and insights based on the user’s data and diagnosis. This research aims to improve early identification and tailored treatment for PCOS by integrating machine learning with a dedicated PCOS app. The goal is to empower individuals to proactively manage their health and well-being.