Efficient Prediction of Heart Disease using SVM Classification Algorithm and Compare its Performance with Linear Regression in Terms of Accuracy

Abstract

Aim: The main objective of this research article is to employ the detection of heart disease by using Support vector machine (SVM) classifier in comparison with Linear regression (LR) model. Materials & Methods: The dataset used in this paper was collected from the UCI machine learning repository database. The sample size for the detection of heart disease was sample 60 (Group 1=30 and Group 2 =30) and calculation was performed utilizing G-power 0.8 with alpha and beta qualities of 0.05, 0.2 with a confidence interval of 95%. The detection of heart disease is performed by the Support Vector Machine (SVM) classifier with a number of samples (N=30) and Linear regression (LR) model with a number of samples (N=30). Results: The Support vector machine (SVM) classifier has a 90.43 percent higher accuracy rate when compared to the accuracy rate of the Linear regression (LR) model is 78.56 percent. The study has
a significance value of p=0.021. Conclusion: Support vector machine (SVM) classifier provides better outcomes in accuracy rate when compared to Linear regression (LR) model for detection of heart disease.