Machine Learning Approaches on Polycystic Ovary Syndrome
Author(s):
Sanjana Mahadik, Vrushali Dhami, Shubhangi Wadibhasme, Rutuja Argade
Keywords:
Polycystic Ovary Syndrome, Support vector machine, Random Forest, Decision Tree and Naive Bayes Classifier.
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.
Article Details
Unique Paper ID: 164974

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2956 - 2961
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