Analysis and Prediction of Polycystic Ovarian Syndrome Using ML Classifiers

Polycystic Ovarian Syndrome (PCOS) is an endocrine disorder which is common in females of reproductive age. A large number of females of reproductive age are affected by PCOS due to adverse change in their lifestyle. As of the current trend between 2.2 and 26.7%, women of childbearing age have PCOS. Visiting a gynaecologist might be a taboo for many women, let alone the cost of running multiple tests. So, using medical expertise and feature selection we have filtered and obtained basic parameters that can predict if a woman has PCOS or not with minimal number of medical tests, thus reducing cost and time. In this study, we have used feature selection and classification algorithms to detect if a woman has PCOS or not. The algorithms used are Logistic Regression and Random Forest of which we have concluded that Random Forest provides a higher accuracy.