Prediction of Stroke by using Machine learning techniques

Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. In this study, we propose early prediction of stroke diseases using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average glucose level, smoking status, previous stroke and age. Using these high features attributes, different classifiers have been trained, namely: Logistics Regression, Random Forest Classifier, K-Nearest Neighbors Classifier, and Support Vector Machine for predicting the stroke. And we observe that Random forest classifier has highest accuracy among them