The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest 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 MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.
Diabetes is a type of disease in which the body fails to regulate the amount of glucose necessary for the body. It does not allow the body to produce or properly use insulin. Diabetes has widespread fallout, with a large people affected by it in world. In this paper; we demonstrate that a fuzzy c-means-neuro-fuzzy rule-based classifier of diabetes disease with an acceptable interpretability is obtained. The accuracy of the classifier is measured by the number of correctly recognized diabetes record while its complexity is measured by the number of fuzzy rules extracted. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
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