A Review on Predicting Brain Stroke using Machine Learning

At present, healthcare is one of the biggest concerns in the world. Brain Stroke is the leading cause of death worldwide. Prediction and detection of the occurrences of a brain stroke at the early stages is a valuable work in the medical field. There are several factors which are responsible for the brain stroke such as BMI (Body Mass Index); Age; Sex; Family Background; Gender; smoking status; hypertension, etc. In healthcare, a lot of research work has been done on the prediction of heart disease, but relatively little attention is paid on predicting a brain stroke. The main aim of this study is to review different research articles published earlier and to choose the best machine learning techniques for the prediction of brain stroke for our future work. After reviewing the different machine learning methods utilized for stroke predictions and after taking into account the previously published studies, it has been discovered that death rate and functional results are the expected outcomes for the majority of the research work done. Support Vector Machine, Stacking, Decision Tree, Weighted Voting, Random Forest, Neural Networks and Naive Bayes were the most frequently employed methods.