Brain Stroke Detection and Prediction Using Machine Learning Approach: A Cloud Deployment Perspective

An ischemic stroke is a medical disorder that happens by ripping of circulation in the mind. A cardiac event can also arise when the circulation supply to the cerebellum is interrupted. It is a main factor in mortality and impairment globally, according to the World Health Organisation. Most work on heart stroke forecasting has been performed, however, few results illustrate the risk as a result of the mental attack. After that, AI algorithms are employed to predict the likelihood of mind strokes. The purpose of this work is to demonstrate whether machine learning may be utilized to foresee the beginning of brain strokes. For accurate prediction, the study used ML calculations such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Navies Bayes (NB), and Support Vector Machine (SVM), and deploy it on the cloud using AWS cloud. The results showed that SVM and LR had a maximum accuracy of 94%.