Comparison of Support Vector Machine Models in the Classification of Susceptibility to Schistosomiasis

Abstract

Schistosomiasis has become epidemic sending millions of people into untimely graves. A lot of contributing efforts in term of research have been made to eradicate or reduce the rate of this dangerous infection. In this research work, the concept of Machine Learning as one of the sub-division of Artificial Intelligence is being used to determine the level of susceptibility of Schistosomiasis. The research made a comparison of the various support vector machine models - Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian and Coarse Gaussian model to determine the level of susceptibility to Schistosomiasis. The results obtained which include Confusion Matrix (CM), Receiver Operating Character (ROC) and Parallel Coordinate Plots (PCP) were interpreted in the form of accuracy, processing speed and execution time. It was finally concluded that Medium Gaussian is the best of all the six models.