A Comparative Analysis of Cardiac Data Classification using Support Vector Machine with Various Kernels

Abstract

Accurate and early diagnosis of cardiac disease is necessary to prevent the death rate. Support vector machine (SVM) is one of the most powerful data classification technique which has been used by the researchers for classifying different types of data. The authors in this paper have compared the performance of SVM with four different types of kernels for classifying cardiac data. The data has been collected from the University of California Irvine (UCI) machine learning repository. From the result, it can be noticed that SVM with the polynomial kernel is performing better as compared to the other three. The proposed result is also compared with some earlier works.