Fig. 1. Immune response. Fig. 3. Input membership functions. Fig. 4. Output membership functions. Representation of condusion matrix where T is the set of data items to be classified (the test set), t € T, t.c is the class of item ¢, and classify(t) returns the classification of t by AIRS. Table 4 Table 5 Classification accuracies obtained with our proposed system and other classifiers from literature We can explain the entries of our confusion matrix: Confusion matrix References From the above results, we conclude that the new hybrid system combining the fuzzy weighting pre-processing and AIRS obtains very promising results in classifying the pos- sible heart disease patients. We believe that the proposed system can be very helpful to the physicians for their final decision on their patients. By using such an efficient tool, they can make very accurate decisions.
