Machine Learning Approach: Detecting Polycystic Ovary Syndrome & It's Impact on Bangladeshi Women

Polycystic Ovary Syndrome is mainly metabolic and reproductive endocrinopathy and an extremely complicated disorder among women. It causes hormonal imbalance, menstruation problems, hair loss, facial acne, facial pimple, dark skin, diabetics, overweight etc. Early detection is important for getting the curation for PCOS. Early detection brings early prevention. In our paper, we were able to collect 550 data of Bangladeshi women. Out of them, 462 are affected by this disorder. We have highlighted common symptoms of PCOS Bangladesh women and it's effect on women. We have used machine learning methods like Random Forest, Naive Bayes Classification, Decision Tree Classification, Super Vector Machine Learning, K-nearest Neighbor, Logistic Regression, XGBoost Classifier, Gradient Boosting Classifier to detect PCOS and comparing which algorithms work best for detecting the PCOS based on our real datasets. Also choose the best model on our real time dataset. We analyze our data set and find out the accuracy, confusion matrix, f1-score, cross validation, recall, precision, support. Among all classification SVM has the highest accuracy. The accuracy of the Super Vector Machine Learning is 99.09%.