A case study for medical decision making with the fuzzy soft sets

  • Murat KirişciEmail author


Molodtsov proposed a completely new approach to modelling uncertainty. This approach is called the soft set theory. Many applications of soft set theory have been developed by combining with a fuzzy set idea. In the present study, for the medical decision-making, the proposed technique related to the fuzzy soft set by Celik–Yamak through Sanchez’s method was used. The real dataset which is called Cleveland heart disease dataset was used in this problem.


Cleveland dataset Decision making Defuzzification Fuzzy soft set Triangular fuzzy number Trapezoidal fuzzy number 

Mathematics Subject Classification

Primary 03E75 Secondary 03E72 68T37 94D05 



I have benefited much from the constructive reports of the anonymous referees, and I am grateful for their valuable comments on the first draft on this paper, which improved the presentation and readability.

Compliance with ethical standards

Conflict of interest

The author declares that he has no conflict of interest.


  1. 1.
    Broumi, S., Smarandache, F., Dhar, M.: On fuzzy soft matrix based on reference function. I.J. Inf. Eng. Electron. Bus. 5(2), 52–59 (2013). CrossRefGoogle Scholar
  2. 2.
    Broumi, S., Majumdar, P., Smarandache, F.: New operations on intuitionistic fuzzy soft sets based on second Zadeh’s logical operators. I.J. Inf. Eng. Electron. Bus. 6(1), 25–31 (2014). CrossRefGoogle Scholar
  3. 3.
    Çelik, Y., Yamak, S.: Fuzzy soft set theory applied to medical diagnosis using fuzzy arithmetic operations. J. Inequal. Appl. 2013, 82 (2013). MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Kirişci, M.: Integrated and differentiated spaces of triangular fuzzy numbers. Fas. Math. 59, 75–89 (2017). MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Maji, P.K., Bismas, R., Roy, A.R.: Fuzzy soft sets. J. Fuzzy Math. 9(3), 677–692 (2001)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Maji, P.K., Roy, A.R., Biswas, R.: An application of soft sets in a decision making problem. Comput. Math. Appl. 44, 1077–1083 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Maji, P.K., Bismas, R., Roy, A.R.: Soft set theory. Comput. Math. Appl. 45, 555–562 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Molodtsov, D.: Soft set theory-first results. Comput. Math. Appl. 37, 19–31 (1999)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Roy, A.R., Maji, P.K.: A fuzzy soft set theoretic approach to decision making problems. J. Comput. Appl. Math. 203, 412–418 (2007)CrossRefGoogle Scholar
  10. 10.
    Sanchez, E.: Inverse of fuzzy relations, application to possibility distributions and medical diagnosis. Fuzzy Sets Syst. 2(1), 75–86 (1979)MathSciNetCrossRefGoogle Scholar
  11. 11.
    UC Irvine Machine Learning Repository, Cleveland heart disease data details (online) (2010). Accessed 9 Sep 2018
  12. 12.
    Zadeh, L.A.: Fuzzy sets. Inf. Comput. 8, 338–353 (1965)zbMATHGoogle Scholar

Copyright information

© African Mathematical Union and Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematical Education, Hasan Ali Yücel Education FacultyIstanbul University-CerrahpaşaIstanbulTurkey

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