An Intuitionistic Fuzzy Set Based Approach to Intelligent Data Analysis: An Application to Medical Diagnosis

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 86 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.