A Review on the Detection Techniques of Polycystic Ovary Syndrome Using Machine Learning

Abstract

Polycystic Ovary Syndrome (PCOS) is a critical hormonal disorder of women that significantly impacts life. In this new generation, women are more prone to PCOS. It is the cause of various problems, including infertility. Early detection of PCOS can reduce complexity. Therefore, an early and proper PCOS detection system is essential to minimize complications. Among all the detection techniques Machine Learning (ML) has an excellent performance in detection for its feature extraction capability. Therefore, considerable research has been carried out to detect PCOS using ML. Various ML approaches like Convolutional Neural Network, Support Vector Machine, K-Nearest-Neighbors, Random Forest, Logistic Regression, Decision Tree, Naive Bayes, etc., are used in detecting PCOS. This research aims to call attention to the researchers by presenting a descriptive and contextual overview of all the existing technologies on PCOS detection by ML algorithms. A comprehensive analysis is carried out of how various ML approaches have been used in PCOS detection over the last few decades, and the techniques are discussed thoroughly. A complete examination was studied on different datasets used in PCOS detection. The performance of several algorithms is compared in quantitative and qualitative approaches. Finally, the significant difficulties and future research scopes are discussed to draw a conclusion.