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.
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Szmidt, E., Kacprzyk, J. (2003). An Intuitionistic Fuzzy Set Based Approach to Intelligent Data Analysis: An Application to Medical Diagnosis. In: Abraham, A., Jain, L.C., Kacprzyk, J. (eds) Recent Advances in Intelligent Paradigms and Applications. Studies in Fuzziness and Soft Computing, vol 113. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1770-6_3
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DOI: https://doi.org/10.1007/978-3-7908-1770-6_3
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