Abstract
Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that affects a large percentage of women of reproductive age. PCOS causes imbalanced or delayed menstrual cycles and produces high levels of the male hormone. The ovaries may create a significant number of little fluid-filled sacs (follicles) yet fail to discharge eggs regularly. The actual cause of PCOS is uncertain. However, early exposure and curing, as well as weight loss, may lower the threat of long-term complications. This study focuses on PCOS diagnosis based on a clinical dataset supplied by Kottarathil, accessible via its Kaggle repository. Non-invasive screening parameters are used to evaluate a range of machine learning approaches for screening PCOS patients without the use of invasive diagnostics. According to the findings of the experiments, the Random Forest (RF) method outperforms the other prominent machine learning algorithms with an accuracy of 93.25%. Further, the out-of-bag (OOB) error is utilized for assessing the prediction performance of RF.