Femina Forecast: Ai-Driven Pcos Prediction

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most prevalent endocrine disorders in women of reproductive age. Timely diagnosis is crucial to avoid complications such as infertility, obesity, type 2 diabetes, and cardiovascular diseases. However, traditional diagnostic methods are time-consuming and may overlook subtle indicators. This paper presents an AI-based model that leverages machine learning techniques to predict the presence of PCOS using clinical and lifestyle parameters. A dataset comprising anonymized patient information was used to train and evaluate multiple machine learning models. Among the models tested, Random Forest provided the best performance with over 90% accuracy. The system is envisioned to be an intelligent support tool that assists healthcare providers in early diagnosis, thereby enabling faster intervention and personalized treatment plans