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Automatic Identification of Diabetes Diseases using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-Nearest Neighbor

The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System 2 (AIRS2) is one of the methods used in medical classification problems. In this paper, we used a Modified AIRS2 (MAIRS2) where we replace the Knearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and AIRS2 with fuzzy K-nn algorithm using 10-fold cross-validation was, respectively 82.69% and 89.10%.