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 %.