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


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%.


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


  1. 1.
    National Diabetes Information Clearinghouse (NDIC).
  2. 2.
    K. Polat, S. Günes. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing, pp. 702–710, 2007.Google Scholar
  3. 3.
    S. W. Purnami, A. Embong, J. M. Zain, S. P. Rahayu, A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis, Journal of Computer Science, pp. 1003–1008, 2009.Google Scholar
  4. 4.
    S. Şahan, K. Polat, H. Kodaz, and S. Güneş. The Medical Applications of Attribute Weighted Artificial Immune System (AWAIS): Diagnosis of Heart and Diabetes Diseases. ICARIS 2005, LNCS 3627, pp. 456–468, 2005.Google Scholar
  5. 5.
    K. Polat and S. Gunes. An improved approach to medical data sets classification: artificial immune recognition system with fuzzy resource allocation mechanism. Expert Systems, pp. 252–270, 2007.Google Scholar
  6. 6.
    B. Ster and A. Dobnikar. Neural networks in medical diagnosis: comparison with other methods. International Conference on Engineering Applications of Neural Networks, pp. 427–430, 1996.Google Scholar
  7. 7.
    L. Nunes de Castro, F. J. Von Zuben. Artificial immune systems: part I—basic theory and applications. Technical Report. TR – DCA 01/99, 1999.Google Scholar
  8. 8.
    L. Nunes de Castro, F. J. Von Zuben.. Artificial immune systems: part II—a survey of applications. Technical Report. TR – DCA 01/99, 1999.Google Scholar
  9. 9.
    J. Timmis, T. Knight, L.N. de Castro and E. Hart. An Overview of Artificial Immune Systems. In Paton R, Bolouri H, Holcombe M, Parish J H, and Tateson R (Eds.) “Computation in Cells and Tissues: Perspectives and Tools for Thought”, Natural Computation Series, pp. 51–86, 2004.Google Scholar
  10. 10.
    U. Aickelin and D. Dasgupta. ARTIFICIAL IMMUNE SYSTEMS. In: Burke E. K.; Kendall G. (Eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 375–399, 2005.Google Scholar
  11. 11.
    Nunes de Castro, L., and Von Zuben, F. J., Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation - TEC 6(3):239–251, 2002.CrossRefGoogle Scholar
  12. 12.
    J. Greensmith, A. Whitbrook, U. Aickelin. Artificial Immune Systems. In Gendreau M, Potvin J.Y. (Eds.) Handbook of Metaheuristics, 2nd edition, pp. 421–448, 2010.Google Scholar
  13. 13.
    A. Watkins, J. Timmis. Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Genetic Programming and Evolvable Machines, pp. 291–317, 2004.Google Scholar
  14. 14.
    A. Watkins, J. Timmis. Artificial Immune Recognition System (AIRS): Revisions and Refinements. In J. Timmis and P.J. Bentley, editors, 1st International Conference on Artificial Immune Systems (ICARIS2002), pp. 173–181, 2002.Google Scholar
  15. 15.
    A. Watkins and L. Boggess. “A New Classifier Based on Resource Limited Artificial Immune Systems”. In Proceedings of Congress on Evolutionary Computation, Part of the 2002 IEEE World Congress on Computational Intelligence held in Honolulu, HI, USA, pp. 1546–1551, 2002.Google Scholar
  16. 16.
    K. Polat, S. Gunes. Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS). Biomedical Signal Processing and Control, pp. 253–260, 2006.Google Scholar
  17. 17.
    K. Polat, S. Güneş. A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system. Expert Systems with Applications, pp. 57–64, 2009.Google Scholar
  18. 18.
    K. Polat, S. Sahan, H. Kodaz, and S. Günes. Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Systems with Application, pp. 172–183, 2007.Google Scholar
  19. 19.
    J.M. Keller, M.R. Gray, J.A. Givens Jr., A fuzzy k-nearest neighbor algorithm, IEEE Trans. Syst. Man Cybern, pp. 580–585, 1985.Google Scholar
  20. 20.
    A. Sengur. An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases. Computers in Biology and Medicine, pp. 329–338, 2008.Google Scholar

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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