Prediction of Stroke Severity in Brain Through Machine Learning

As per the statistics of world health organization, the death rate in adolescents and young adults is found to be 1.3 million in 2020. Increase in health issues among young adults and adolescents is observed at higher rates in the last decade. Brain stroke is one of the diseases which is silently making its progress from occurring across senior citizens to young adolescents. The point of the work is to decrease the possibility of brain stroke or better forestall them by taking input from the client and foreseeing the result considering the information entered. The entire process is done by utilizing AI methods and the functioning framework has acquired a precision of roughly 94.2%. Five unique classifiers namely Decision tree, KNN, Naive Bayes, Logistic regression and SVM are used in building vulnerability predictors and utilized in a few comparative explorations work. Additionally, these models are assessed by estimating the disarray grids by using Dataset from Kaggle.