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

Abstract

Schistosomiasis has become endemic sending millions of people into untimely graves. A lot of contributing efforts in term of research has 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 as useful tools in the Machine Learning to determine the level of susceptibility of Schistosomiasis. The results obtained which include Confusion Matrix (CM), Receiver Operating Character (ROC), and Parallel Coordinate Plot were interpreted in form of accuracy, processing speed and execution time. It was finally concluded that Medium Gaussian is the best of all the six models considered.