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Similarity Measures of Intuitionistic Fuzzy Sets for Cancer Diagnosis: A Comparative Analysis

  • Lazim AbdullahEmail author
  • Sook Wern Chan
Conference paper

Abstract

Membership degree, non-membership degree and hesitancy degree are the three components that characterized the intuitionistic fuzzy sets (IFSs). Based on information inherited from the three membership degrees, this paper proposes Cosine Similarity Measures (CSM) and Jaccard Similarity Measures (JSM) between IFSs and their application to a case of cancer diagnosis. Three experts in medical fraternity were invited to provide linguistic evaluation pertaining to symptoms with respect to types of cancer diseases, and patients with respect to symptoms using linguistic terms that defined in IFSs. The information of symptoms and type of cancer diseases for each patient was collected and then computed using CSM. The similar set of information was iterated using JSM. A comparative analysis of similarity measures between CSM and JSM is presented to illustrate the consistency of the two similarity measures of IFSs. It is shown that the two similarity measures are consistent in suggesting a cancer diagnosis despite differences in mathematical formulations.

Keywords

Similarity measures Intuitionistic fuzzy sets Cancer diagnosis Decision making Fuzzy relation 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Informatics and Applied MathematicsUniversiti Malaysia TerengganuKuala TerengganuMalaysia

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