• Meghraj Walkikar
• Amruta Nevare
• Shweta Bansode
• Aasawari Mohite
• Prof. Dr. Geetha Chillarge

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, characterized by hormonal imbalances, irregular menstruation, and ovarian cysts. Timely identification of PCOS is crucial for effective management and mitigating long-term complications. This study proposes an innovative machine learning approach for PCOS detection, utilizing a dataset containing clinical and biochemical features. The dataset encompasses demographic details, medical history, and hormonal profiles of both PCOS-diagnosed patients and healthy individuals. Diverse machine learning algorithms, including logistic regression, random forest, and support vector machines, are deployed to formulate predictive models for PCOS detection. Feature selection techniques are applied to discern the most relevant features for model training. The experimental findings underscore the efficacy of the proposed approach in accurately identifying PCOS. The developed models exhibit notable accuracy, sensitivity, and specificity, suggesting their potential utility as diagnostic tools for PCOS. This research significantly contributes to advancing PCOS diagnosis by harnessing machine learning techniques to enhance early detection and management of this prevalent condition.

Polycystic Ovary Syndrome, Menstrual irregularity, Polycystic ovaries, Clinical features

"Prediction and Detection of PCOS using Machine learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a259-a262, March-2024, Available :http://www.jetir.org/papers/JETIR2403034.pdf

"Prediction and Detection of PCOS using Machine learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa259-a262, March-2024, Available at : http://www.jetir.org/papers/JETIR2403034.pdf