Smart Health Care: Machine Learning for PCOS Detection and Prediction

Abstract: Today, Polycystic Ovary Syndrome (PCOS) is found in numerous women, so it is a common issue. PCOS is a hormonal disorder that leads to delayed, irregular, or absent menstrual cycles in the female body. This syndrome may cause the growth of type 2 diabetes, gestational diabetes, weight gain, excess body hair, and many other complications. In advanced cases, PCOS may lead to infertility, which is a challenge for patients who are attempting to conceive. According to statistics, the rate of incidence of PCOS in recent years has greatly increased, which is alarming. If PCOS is diagnosed early enough, individuals can adhere to their physician's advice and live a healthier life. The data collection in this study includes records of 466 patients. The purpose of this study is to utilize machine learning models to determine patterns in this disorder. The learned information is then fed into different algorithms to determine accuracy, specificity, sensitivity, and precision using different ML models, including Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) among others. The study used the Mutual Information model for feature selection and compared models to identify the most precise one. Using the Mutual Information model for feature engineering, AB and RF attained the highest accuracy of 94 %.