Abstract
POLYCYCTIC OVARY SYNDROME (PCOS) DETECTION USING MACHINE LEARNING
*Priyanka H. L., Krithi K. N., Spoorthi C. N., Shruthi B. S., Sanchitha P. and Shreya Gowda N. R.
ABSTRACT
Polycystic Ovary Syndrome (PCOS) is a really not a rare endocrine ailment, affecting ladies of reproductive age. Early detection and diagnosis is quite crucial for effective management and prevention of various related complications. In this paper, we are strongly recommending a pretty integrated approach, making use of some machine learning algorithms and deep learning techniques specifically for PCOS detection. The quite interesting study employs the good old okay-Nearest pals (KNN), the assist Vector machine (SVM), not thelegendary Logistic Regression, and the mind- blowing Gaussian Naive Bay algorithms to analyze the clinical data from a curated PCOS dataset. Moreover, are introducing the not-so-basic deep learning techniques, just to process ovarian pics for an enhanced diagnostic accuracy. The proposed method clearly demonstrates some super promising, mind blowing and jaw-dropping outcomes in PCOS detection and also holds some kind significant, gigantic and tremendous potential for improving early diagnosis and treatment effects.
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