PCOS Detection and Monitoring using Machine Learning
Bhargavi Nimmala, Udaya Deepthi Nimmala, Akhilesh Elangi, Shilpa Bagade
Abstract
A common endocrine condition affecting women who are of reproductive age is called polycystic ovarian syndrome, or PCOS. Hormonal abnormalities, irregular menstrual cycles, and the development of ovarian cysts are characteristics. PCOS has an impact on one’s health and well-being throughout life, which includes preventing major illnesses like high blood pressure, heart and blood vessel problems, type-2 diabetes, uterine cancer and infertility. A combination of genetics, hormone abnormalities, stress, environment, and lifestyle choices is probably what causes PCOS. Early detection and ongoing monitoring are necessary to promote improved management and prompt intervention for PCOS. The primary goal of this research study is to use symptoms to determine if a woman has PCOS. Machine Learning (ML) algorithms like SVM, Random Forest, Decision Tree, Gaussian Naive Bayes and Logistic Regression can be used to identify it. This study has compared each model’s output and the best model was applied for predictions. Additionally, an online application was developed that allows patients to obtain exercise and food plans, both of which are helpful in improving their PCOS condition. Patients can also get answers to their inquiries from a chatbot, and in case of any emergency, they can contact the virtual doctor.