A Case Study of the Application of WEKA Software to Solve the Problem of Liver Inflammation

Abstract

Željko Đ Vujović*

This paper aimed to consider the reliability of the basic metrics of evaluation of classification models: accuracy, sensitivity, specificity, and precision. The WEKA software tool was applied to the “Hepatitis C Virus (HCV) for Egyptian patient’s dataset”. The algorithms Bayesnet, Naivebayesh, Multilayer Perceptron, J48, and 10-fold crossvalidation were used in the study. The main results obtained are that, with all four algorithms in question, they achieved approximately the same accuracy of correctly classified specimens. BaiesNet-22.96%, Naïve Baies-26.14%, MultilaierPerceptron -26.57% and J48-25.27%. Binary classification metrics-sensitivity, specificity, and precision show very different values, depending on the intended class. Metric specificity, for all four algorithms, shows that a value that is in most of the range of possible values (0-1). Metric sensitivity and precision, for all four algorithms, showed values that are in the lower part of the range of possible values (0-1). The results of this study showed that WEKA software could not yet be considered as a relevant tool for the diagnosis of Hepatitis C Virus, on whose data set it was applied.