Abstract: Polycystic Ovary Disorder (PCOD) is one of the most common hormonal disorders affecting women of reproductive age. Early detection and continuous monitoring are essential to prevent severe long-term health complications such as infertility, diabetes, and cardiovascular diseases. However, traditional diagnosis methods rely heavily on manual medical analysis and delayed clinical evaluation. This paper proposes an AI-based PCOD SmartCare System that uses Machine Learning algorithms to predict the risk level of PCOD and provide personalized health recommendations. The system collects patient health data including symptoms, hormone levels, and medical reports. Logistic Regression and Random Forest algorithms are used to analyse the data and classify risk levels as Normal, Moderate, or High. Based on the prediction results, the system generates customized diet plans, lifestyle suggestions, and exercise recommendations. The proposed solution aims to assist early diagnosis, reduce medical costs, and improve women’s healthcare using an intelligent and user-friendly platform.

Keywords: PCOD, Machine Learning, Healthcare AI, Logistic Regression, Random Forest, Disease Prediction, Women Health, Smart Healthcare System

Downloads: | DOI: 10.17148/IARJSET.2026.13232