Polycystic Ovary Syndrome Classification Based on Machine Learning Techniques: A Comparative Analysis
Nancy Girdhar, Priya Singh, Tisha Singhal · Apple Academic Press eBooks · 2024
Polycystic ovary syndrome (PCOS) is an ailment predominantly occurring in females of reproductive age ranging from 15 years to 45 years, that is, the time span between menopause and menarche. The root cause is unknown but medical symptoms show that the disease causes the excessive production of male hormone in the female body resulting in cysts in the ovaries of women. The disturbances in the hormones lead to various drastic symptoms including hirsutism, male pattern baldness, type 2 diabetes, endometrial cancer, and irregular menstrual cycle causing infertility and complications in pregnancy. This chapter focuses on the diagnosis classification of the syndrome on the selected set of features that are most associated with the disease adopting various machine learning algorithms. Algorithms applied to the dataset are logistic regression, Gaussian-naïve Bayes, K-nearest neighbors, random forest, decision tree classifier, and Xtreme gradient boosting and then a comparison is drawn of these machine learning algorithms to find out the best performing model in the classification of the PCOS patients.