An intuitionistic fuzzy set based approach to intelligent data analysis: an application to medical diagnosis

  • Authors:

  • Eulalia Szmidt;Janusz Kacprzyk

  • Affiliations:

  • Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland;Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

  • Venue:
  • Recent advances in intelligent paradigms and applications
  • Year:
  • 2003

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Abstract

We propose a new approach for medical diagnosis by employing intuitionistic fuzzy sets (cf. Atanassov [1]; [2]) which because of additional degree of freedom in comparison with fuzzy sets (Zadeh [14]), can be viewed as their generalization. Employing intuitionistic fuzzy sets, we can simply and adequately express a hesitation concerning the objects considered - both patients and illnesses. Solution is obtained by looking for the smallest distance (cf. Szmidt and Kacprzyk [8], [11]) between symptoms that are characteristic for a patient and symptoms describing illnesses considered. We point out advantages of this new technique over the method proposed by De, Biswas and Roy [4] where intuitionistic fuzzy sets were also applied but the max-min-max composition of intuitionistic fuzzy relations was used instead of taking into account all, unchanged symptom values as proposed in this article.