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SW-002 Scopus by SUPHAL BASAK is a document available to read on EtoBox.

This paper presents a multi-level machine learning approach for the early detection of Polycystic Ovary Syndrome (PCOS), achieving 94.5% accuracy using algorithms like Random Forest, XGBoost, Naive Bayes, and AdaBoost. The study emphasizes the importance of machine learning in overcoming traditional diagnostic challenges associated with PCOS, which often leads to misdiagnosis and delayed treatment. The proposed methodology incorporates data preprocessing, class imbalance handling, and model interpretability

Author

SUPHAL BASAK

Language

EN