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Issue:Medical diagnosis in intuitionistic fuzzy context: Difference between revisions

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  | author          = Shiny Jose
  | author          = Shiny Jose
  | institution    = Department of Mathematics, St. George College
  | institution    = Department of Mathematics, St. George College
  | address        = University of Ibadan, Nigeria
  | address        = Aruvithura, Kerala, India
  | email-before-at = shinyjosedavis
  | email-before-at = shinyjosedavis
  | email-after-at  = gmail.com
  | email-after-at  = gmail.com
Line 26: Line 26:
  | file            = NIFS-25-1-59-63.pdf
  | file            = NIFS-25-1-59-63.pdf
  | format          = PDF
  | format          = PDF
  | size            = 185 Kb
  | size            = 82 Kb
  | abstract        = In this paper, we introduce a distance formula for intuitionistic fuzzy sets called novel tangent inverse distance and found that it satisfies the properties of a distance measure. Application of tangent inverse distance in medical diagnosis is also discussed.
  | abstract        = In this paper, we introduce a distance formula for intuitionistic fuzzy sets called novel tangent inverse distance and found that it satisfies the properties of a distance measure. Application of tangent inverse distance in medical diagnosis is also discussed.
  | keywords        = Intuitionistic fuzzy sets, Tangent inverse distance, Medical diagnosis.
  | keywords        = Intuitionistic fuzzy sets, Tangent inverse distance, Medical diagnosis.

Latest revision as of 18:13, 23 April 2019

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http://ifigenia.org/wiki/issue:nifs/25/1/59-63
Title of paper: Medical diagnosis in intuitionistic fuzzy context
Author(s):
Jeswin B. George
Department of Mathematics, BVM Holy Cross College, Cherpunkal, Kerala, India
jeswinkomarathakunnel@gmail.com
Shiny Jose
Department of Mathematics, St. George College, Aruvithura, Kerala, India
shinyjosedavis@gmail.com
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 25 (2019), Number 1, pages 59–63
DOI: https://doi.org/10.7546/nifs.2019.25.1.59-63
Download:  PDF (82 Kb  Kb, File info)
Abstract: In this paper, we introduce a distance formula for intuitionistic fuzzy sets called novel tangent inverse distance and found that it satisfies the properties of a distance measure. Application of tangent inverse distance in medical diagnosis is also discussed.
Keywords: Intuitionistic fuzzy sets, Tangent inverse distance, Medical diagnosis.
AMS Classification: 03E72.
References:
  1. Atanassov, K. T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1), 87–96.
  2. Atanassov, K. T. (1989). More on Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 33 (1),37–45.
  3. De, S. K., Biswas, R., & Roy, A. R. (2001). An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets and Systems, 117 (2), 209–213.
  4. Edward Samuel, A. & Narmadhagnanam, R. (2018). Intuitionistic Fuzzy Sets in Medical Diagnosis, International Journal of Mathematical Archive, 9 (1), 1–5.
  5. Ejegwa, P. A., Onoja, A. M., & Emmanuel, I. T. (2014). A Note on Some Models of Intuitionistic Fuzzy Sets in Real Life Situations, Journal of Global Research in Mathematical Archives, 2 (5), 42–50.
  6. Intarapaiboon, P. (2014). New Similarity Measures for Intuitionistic Fuzzy Sets, Applied Mathematical Sciences, 8 (45), 39–50.
  7. Szmidt, E., & Kacprzyk J. (2002). An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis. Recent Advances in Intelligent Paradigms and Applications (A. Abraham, L. Jain, J. Kacprzyk, Eds), Springer, Berlin, 57–70.
  8. Szmidt, E., & Kacprzyk J. (2005). A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. Lecture Notes in Artificial Intelligence, Vol. 3070, Springer, Berlin, 388–393.
  9. Todorova, L., Atanassov, K., Hadjitodorov, S., & Vassilev, P. (2007). On an intuitionistic fuzzy approach for decision making in medicine: Part 1, Int. Journal Bioautomation, 6, 92–101.
  10. Todorova, L., Atanassov, K., Hadjitodorov, S., & Vassilev, P. (2007). On an intuitionistic fuzzy approach for decision making in medicine. Part 2. Int. Journal Bioautomation, 7, 64–69.
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