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Journal of Medical Systems
Published in: Journal of Medical Systems 5/2012

01-10-2012 | ORIGINAL PAPER

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

Authors: Mohamed Amine Chikh, Meryem Saidi, Nesma Settouti

Published in: Journal of Medical Systems | Issue 5/2012

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Abstract

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%.
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Metadata
Title
Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor
Authors
Mohamed Amine Chikh
Meryem Saidi
Nesma Settouti
Publication date
01-10-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 5/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9748-4

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