Comparative Analysis of Polycystic Ovary Syndrome Detection Using Machine Learning Algorithms

Abstract

INTRODUCTION: Polycystic Ovary Syndrome is a condition in which the ovaries manufacture androgen, seen in small traces, resulting in the production of cysts. Menstrual cycle abnormalities, clinical and/or biochemical hyperandrogenism, and the presence of polycystic ovaries on ultrasound should all be used to diagnose PCOS. PCOS appears to be a multifaceted illness influenced by both genetic and environmental factors and the symptoms include excessive hair on the face and body, weight gain, voice changes, skin type changes, and irregular periods.

OBJECTIVES: This is the objective of this paper is to identify PCOS in its initial stage.

METHODS: To address this issue the study proposes a comparison of various machine learning algorithms and optimization techniques Among which GSCV gave the best result of 94% accuracy, followed by TPOT with 91% accuracy. Additionally, we also applied Feature selection methods to eliminate zero-importance features to increase the accuracy of algorithms.

RESULTS: The main results obtained in this paper This study explored various Feature selection techniques, ML and DL models. It is shown that Grid Search CV and TPOT classifier were best classifiers with 94% and 91% respectively.

CONCLUSION: These are the conclusions of this paper and this study will explore various DL methodologies and try to find out best optimal results for the PCOS Detection. And also, to develop an PCOS detection application to keep track of menstrual cycles and track activities and symptoms for PCOS.