Prediction of hepatitis disease based on principal component analysis and artificial immune recognition system

In this study, prediction of hepatitis disease, which is a very common and important disease, was conducted with principal component analysis (PCA) and artificial immune recognition system (AIRS). The proposed approach consists of two stages. Firstly, the feature number of hepatitis disease dataset was reduced to 5 from 19 by principal component analysis (PCA). Secondly, hepatitis disease dataset is normalized in the range of [0, 1]. Normalized input values is classified by using AIRS classifier system. We took the dataset used in our study from the UCI Machine Learning Database. The obtained classification accuracy of our system was 94.12% using 10-fold cross-validation and it was very promising with regard to the other classification applications in Literature for this problem. Testing results were found to be compliant with the expected results that are derived from the physician’s direct diagnosis. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for hepatitis disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.