Prediction of Heart Stroke Using Support Vector Machine Algorithm

This paper focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing.