A study on Comparative Performance of SVM Classifier Models with Kernel Functions in Prediction of Hypertension

This paper investigates the ability of several models of Support Vector Machines (SVMs) with alternate kernel functions to predict the probability of occurrence of Essential Hypertension (HT) in a mixed patient population. To do this a SVM was trained with 13 inputs (symptoms) from the medical dataset. Different kernel functions, such as Linear, Quadratic, Polyorder (order three), Multi Layer Perceptron (MLP) and Radial Basis Function kernel (RBF) were coded and tested. A detailed database, comprising healthy and diabetic patients from a university hospital was used for training the SVM for prediction. All five kernel function SVM structures tested showed reasonably good accuracy in prediction of disease (s), with linear kernel structure showing best prediction in 3 out of 4 datasets and Polyorder in one database. Thus the best choice appears to be situation specific. Keywords— Support Vector Machine, Classification, Kernel functions, Hypertension