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Shikha Maheshwari, Amit Srivastava. STUDY ON DIVERGENCE MEASURES FOR INTUITIONISTIC FUZZY SETS AND ITS APPLICATION IN MEDICAL DIAGNOSIS[J]. Journal of Applied Analysis & Computation, 2016, 6(3): 772-789. doi: 10.11948/2016050
Citation: Shikha Maheshwari, Amit Srivastava. STUDY ON DIVERGENCE MEASURES FOR INTUITIONISTIC FUZZY SETS AND ITS APPLICATION IN MEDICAL DIAGNOSIS[J]. Journal of Applied Analysis & Computation, 2016, 6(3): 772-789. doi: 10.11948/2016050
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STUDY ON DIVERGENCE MEASURES FOR INTUITIONISTIC FUZZY SETS AND ITS APPLICATION IN MEDICAL DIAGNOSIS

  • Department of Mathematics, Jaypee Institute of Information Technology, Noida(Uttar Pradesh), India

    Abstract
  • As far as medical diagnosis problem is concerned, predicting the actual disease in complex situations has been a concerning matter for the doctors/experts. The divergence measure for intuitionistic fuzzy sets is an effective and potent tool in addressing the medical decision making problems. We define a new divergence measure for intuitionistic fuzzy sets (IFS) and its interesting properties are studied. The existing divergence measures under intuitionistic fuzzy environment are reviewed and their counter-intuitive cases has been explored. The parameter α is incorporated in the proposed divergence measure and it is defined as parametric intuitionistic fuzzy divergence measure (PIFDM). The different choices of the parameter α provide different decisions about the disease. As we increase the value of α, the information about the disease increases and move towards the optimal solution with the reduced in the uncertainty. Finally, we compare our results with the already existing results, which illustrate the role of the parameter α in obtaining the optimal solution in the medical decision making application. The results demonstrate that the parametric intuitionistic fuzzy divergence measure (PIFDM) is more comprehensive and effective than the proposed intuitionistic fuzzy divergence measure and the existing intuitionistic fuzzy divergence measures for decision making in medical investigations.
    MSC: 94A17;94A15;03E72
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    • References
  • [1] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1986), 87-96.

    Google Scholar

    [2] F. E. Boran and D. Akay, A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition, Information Sciences, 255(2014)(10), 45-57.

    Google Scholar

    [3] S. K. De, R. Biswas and A. R. Roy, An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets and Systems, 117(2001)(2), 209-213.

    Google Scholar

    [4] W. L. Gao and J. B. Danied, Vague sets, IEEE Transactions on Systems, Man, and Cybernetics, 23(1993), 610-614.

    Google Scholar

    [5] K. C. Hung, Medical Pattern Recognition:Applying an Improved Intuitionistics Fuzzy Cross-Entropy Approach, Advances in fuzzy Systems, 863549(2012).

    Google Scholar

    [6] W. L. Hung and M. S. Yang, On the J-Divergence of intuitionistic fuzzy sets with its applications to pattern recognition, Information sciences, 178(2008)(6), 1641-1650.

    Google Scholar

    [7] M. Junjun, Y. Dengbao and W. Cuicui, A novel cross-entropy and entropy measures of IFSs and their applications, Knowledge-Based Systems, 48(2013), 37-45.

    Google Scholar

    [8] C. M. Own, Switching between type-2 fuzzy sets and intuitionistic fuzzy sets:an application in medical diagnosis, Applied Intelligence, 31(2009)(3), 283-291.

    Google Scholar

    [9] E. Szmidt and J. Kacprzyk, Similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning, Artificial Intelligence and Soft Computing -ICAISC, 3070(2004), 388-393.

    Google Scholar

    [10] E. Szmidt and J. Kacprzyk, Intuitionistic fuzzy sets in intelligent data analysis for medical diagnosis, Proceedings of the Computational Science ICCS, Springer, Berlin, Germany, 2074(2001), 263-271.

    Google Scholar

    [11] E. Szmidt and J. Kacprzyk, Intuitionistic fuzzy sets in some medical applications, Proceedings of the 7th Fuzzy Days, 2206 Computational intelligence:theory and applications, Springer, Berlin, Germany, 2001.

    Google Scholar

    [12] I. B. Turksen, Interval valued fuzzy sets based on normal forms, Fuzzy Sets Systems, 20(1986), 191-210.

    Google Scholar

    [13] K. Vlachos and G. D. Sergiadis, Intuitionistic fuzzy information:applications to pattern recognition, Pattern Recognition Letters, 28(2007)(2), 197-206.

    Google Scholar

    [14] C.P. Wei, P. Wang and Y. Z. Zhang, Entropy, similarity measure of intervalvalued intuitionistic fuzzy sets and their applications, Information Sciences, 181(2011), 4273-4286.

    Google Scholar

    [15] P. Wei and J. Ye, Improved intuitionistic fuzzy cross-entropy and its application to pattern recognition, International Conference on Intelligent Systems and Knowledge Engineering, (2010), 114-116.

    Google Scholar

    [16] M. Xia and Z. Xu, Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment, Information Fusion, 13(2012)(1), 31-47.

    Google Scholar

    [17] L. A. Zadeh, Fuzzy sets, Information and Control, 8(1965)(3), 338-353.

    Google Scholar

    [18] Q. S. Zhang and S. Y. Jiang, A note on information entropy measures for vague sets and its applications, Information Sciences, 178(2008)(21), 4184-4191.

    Google Scholar

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