Ai-Based Prediction System for PCOS and Thyroid Disorders in Women using Machine Learning

Polycystic Ovarian Syndrome (PCOS) and thyroid disorders are two of the most commonly discussed endocrine conditions in women, and in practice their symptoms often overlap, which makes an early, informal assessment genuinely difficult. In this project we built and actually ran a machine-learning-based screening tool that looks at a patient's profile along with PCOS-related and thyroid-related readings, and returns a preliminary, model-based assessment through a Gradio web interface. Decision Tree and Random Forest were used as the two classification approaches, chosen because one gives us an easy-to-read baseline and the other gives a sturdier ensemble alternative for structured, tabular data of this kind. Beyond the prediction itself, the interface also surfaces nearby specialist suggestions, a chart showing which inputs contributed most to the assessment, and some basic diet/yoga guidance. We include actual screenshots of the running application in this paper. A sample case a 25-year-old patient from Anekal taluk, Bengaluru Urban returned a PCOS: NEGATIVE and THYROID: NEGATIVE result, which is reported here purely as a demonstration of the working pipeline and not as a measure of the model's real-world accuracy. Formal accuracy, precision, recall and F1-score figures are left to be filled in once the final experimental run on the complete dataset is available, rather than being guessed at. Overall, the system is meant to work as an academic decision-support prototype and a starting point for further validation, and not as a substitute for laboratory testing or a qualified doctor's opinion.