Prediction of Heart Stroke Using Support Vector Machine Algorithm
Harshita Puri, Jhanavi Chaudhary, Kulkarni Rakshit Raghavendra, Rhea Mantri, Kishore Bingi
Abstract
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.
Topics & Concepts
Support vector machineQuadratic equationStroke (engine)Computer scienceAlgorithmMachine learningArtificial intelligenceQuadratic programmingMathematicsMathematical optimizationEngineeringMechanical engineeringGeometryArtificial Intelligence in HealthcareAcute Ischemic Stroke Management