The Effect of SVM Kernel Functions on Heart Disease Dataset

Abstract

The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors, the healthcare environment is still information rich but knowledge poor. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The project is mainly concerned about using the Support Vector Machine (SVM) and its kernels to predict heart disease. Each and every kernel uses different mathematical functions to identify the hyperplane. The hyperplane is usually a line or a plane that separates the instances.

Keywords: Support Vector Machine, scaling, machine learning, Radial Basis, Linear Kernel, Sigmoid Kernel, Polynomial Kernel.