Medical decision support system based on artificial immune recognition immune system (AIRS), fuzzy weighted pre-processing and feature selection

In this study, diagnosis of hepatitis disease, which is a very common and important disease, was conducted with a machine learning system. The proposed machine learning approach has three stages. The first stage, the feature number of hepatitis disease dataset was reduced to 10 from 19 in the feature selection (FS) sub-program by means of C 4.5 decision tree algorithm. Then, hepatitis disease dataset is normalized in the range of [0, 1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can improved by ours, is a new method and firstly, it is applied to hepatitis disease dataset. We took the dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 94.12% and it was very promising with regard to the other classification applications in the literature for this problem.