Journal of Medical Systems

, Volume 36, Issue 5, pp 2721–2729 | Cite as

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

  • Mohamed Amine Chikh
  • Meryem Saidi
  • Nesma Settouti
ORIGINAL PAPER

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%.

Keywords

Pima Indians diabetes data set Diagnosis AIRS2 Fuzzy k- nearest neighbors 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Mohamed Amine Chikh
    • 1
  • Meryem Saidi
    • 1
  • Nesma Settouti
    • 1
  1. 1.Biomedical Engineering LaboratoryTlemcen UniversityTlemcenAlgeria

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