A Data-Driven Approach to Polycystic Ovary Syndrome Diagnosis: Evaluating Machine Learning Models

    July 2025
    Pedram Mohammadi, Najmeh Parvaz, Mohammad Masoud Eslam, Sara Zareei
    Studysummary This study found that using machine learning models, particularly Random Forest with 93% accuracy and 86% sensitivity, can effectively predict PCOS by analyzing features like antral follicle count, hair growth, and skin pigmentation, offering a promising alternative to traditional diagnostic methods.
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