Comparison of Stroke Classification on Support Vector Machine with Backpropagation Neural Network

Abstract

— Stroke is a serious neurological disease that can cause
long-term health impacts if not detected and treated quickly.
Early detection is crucial for effective prevention and treatment.
In this research, two methods were used, Support Vector Machine
(SVM) and Backpropagation, to predict the possibility of stroke.
The comparison results for Backpropagation training show a
higher accuracy of 0.939 compared to SVM of 0.739, while the
comparison of Backpropagation testing shows a higher accuracy
of 0.879 compared to SVM of 0.787