A new intelligent hepatitis diagnosis using principal component analysis and classifiers fusion

 Introduction: In recent years, hepatitis diseases have become prevalent in the world. The correct diagnosis of hepatitis disease is not a straight task. The goal of this paper is to introduce a new intelligent system for automatic hepatitis diagnosis based on machine learning approaches. Materials and Methods: the proposed approach consists of three stages, namely dimension reduction, classification, and fusion of classifiers. The hepatitis disease features were obtained from UCI machine learning repository. First, features have been normalized. Then, the number of these features is reduced to 10 from 19 by principal component analysis. In the next step, the reduced features are fed to three classifiers. Finally, a classifiers fusion to improve the efficiency and more reliable results using majority voting is presented. Results: the proposed approach obtained a classification accuracy of 96.32 via 10 fold cross validation.   Conclusion: according to the results, the proposed system can be used as an intelligent partner for the final hepatitis diagnosis by physician.