Authors : Deepika Rani; Aditya Kumar Singh; Er. Archana Kumari

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/4wejm7mx

Scribd : https://tinyurl.com/yxjfmtzw

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV752

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Polycystic Ovary Syndrome or PCOS is an endocrine disorder affecting a great number of women, mainlyduring their reproductive years, and often leads to infertility and other major health issues. Typically, standard diagnosis forPCOS involves a combination of tests and examinations, which may be laborious and stressful for the patient. The clinical andmetabolic data- based early detection data-driven, automated system for PCOS has been derived from this study. For analysis purposes, the signals are decomposed using wavelet transform.Machine learning algorithms are then applied in the detection of PCOS, a common endocrine disorder that is commonly occurring among women of reproductive age. PCOS is characterized by various symptoms, among them including ovarian cysts, and hormonal imbalances such that the levels of male hormones (androgens) increase while the menstrual periods become irregular. Early detection of PCOS can assist inthe management and reduction of risks associated with it, including infertility, diabetes, cardiovascular diseases, and endometrial cancer. values of heart rate variability

Keywords : PCOS, SVM Classifier, K-Means Clustering, KNN , Logistic Regression, Linear Regression

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