Stroke Prediction Model Using Machine Learning Method

Majority of strokes are brought on by an unanticipated obstruction of blood flow to the brain and heart. Stroke severity can be reduced by being aware of the various stroke warning signs in advance. Previous study on stroke prediction had an accuracy less than 90%. Sample size of 1000 – 2000 for that study was insufficient to justify the results obtained by the trained model. In this study, comparisons are made among different approaches to the stroke prediction model, include four different classification methods, which are logistic regression, Random Forest, Decision Tree and Support Vector Machine (SVM). The results obtained by the classifiers were trained with 2000 samples and 3109. All the classifiers were then tested individually. The accuracy for each model are, 91% for Decision Tree, 95% for Random Forest, 95% for Logistic Regression and 100% Support Vector Machine (SVM). As a conclusion, our study suggested that SVM approach is fit well for stroke prediction model as it achieved the highest accuracy compared to the others.