Machine Learning Solutions to Polycystic Ovary Syndrome: A Review

Polycystic Ovary Syndrome (PCOS) is one of the most popular endocrine disorders. It is a kind of disorder which generally occurs in women when they are in their reproductive age. Women who suffer from PCOS can experience totally absence of menses, unstable menstrual cycles and as well as the rise in androgens or sex hormones. The ovaries will have many follicles at a time and they fail to ovulate an egg every month and may also lead to infertility. The major causes which lead to the syndrome are unclear till date. On the other hand, if the syndrome is not diagnosed early, it can lead to more serious diseases which include diabetes-2 and diseases related to the heart. The major objective of this paper is to review on the application of all the emerging technologies such as machine learning (ML), Artificial Intelligence (AI) in the detection of the PCOS. It will enable researcher to help in knowing what changes can be made in the technologies or what new technologies can be adopted to detect the PCOS. This research paper discussed the advantages, disadvantages and future direction of emerging technologies application in PCOS detection and prediction.