Polycystic Ovary Syndrome Monitoring using Machine Learning
Reddy Richa Vasavi, Seguri Priscilla Prathibha, Hima Bindu Valiveti, Srinidhi Maringanti, Akhila Parsa
Abstract
Polycystic Ovary Syndrome (PCOS) relates to the small cysts that form in the ovaries. PCOS affects health and well-being over the lifespan, rooting out certain critical health issues like high blood pressure, problems with the heart and blood vessels, type 2 diabetes and uterine cancer. Besides, the high widespread of PCOS, many women remain underdiagnosed. Modern Technologies could be used to alter this situation by helping women monitor their lifestyle. The main aim of our project is to build an application to monitor women’s menstruation conditions. The proposed method uses machine learning techniques for predicting the possibility of being diagnosed with a PCOS, based on the symptoms. App Development tools to develop an application to monitor their lifestyle with an additional feature to examine the user’s mental wellness by using GSR (Galvanic Skin Response) sensor.