Comparative Analysis on Brain Stroke Prediction using Machine Learning

Brain Stroke is a disorder that occurs when brain's blood vessels become clogged, resulting brain to be destructed. When there is insufficient blood flow to the brain, it could be a potential cause of stroke. Timely recognition of diverse warning indications of stroke can aid in lessening the intensity of stroke. Well timed detection of stroke indicators can be a life rescuer. Machine Learning now a days is a prevalent strategy that can be used competently in this domain for preliminary detection of stroke inflicting signs. This paper compares and contrasts various machine Learning algorithms namely Random Forest Classifier(RF), Logistic Regression(LR), Support Vector Machine(SVM), K-Nearest Neighbor(KNN), Decision Tree(DT), XGBoost Classifier(XGB) and Naive Bayes(NB). XGB algorithm turned to be the best, attaining an accuracy charge of about 95.38%