Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{164974,
        author = {Sanjana Mahadik and Vrushali Dhami and Shubhangi Wadibhasme and Rutuja Argade},
        title = {Machine Learning Approaches on Polycystic Ovary Syndrome},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2956-2961},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164974},
        abstract = {Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women worldwide. It is characterized by a complex interplay of hormonal imbalances, metabolic dysfunction, and ovarian abnormalities. Early detection and diagnosis of PCOS are crucial for timely intervention and management of the condition. This abstract presents an overview of various approaches and advancements in PCOS detection, highlighting both traditional and emerging methods. The traditional diagnostic criteria for PCOS include the Rotterdam criteria, which require the presence of at least two out of three features: irregular menstrual cycles, clinical or biochemical signs of hyper androgenism, and polycystic ovaries observed on ultrasound. However, these criteria have limitations, and newer diagnostic strategies are being explored Keywords: Predictive modeling, Model Training, Diagnosis, Machine Learning.},
        keywords = {Polycystic Ovary Syndrome, Support vector machine, Random Forest, Decision Tree and Naive Bayes Classifier.},
        month = {},
        }