Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition

  • Authors:

  • Vahid Khatibi;Gholam Ali Montazer

  • Affiliations:

  • Information Technology Department, School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran;Information Technology Department, School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2009

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Abstract

Objective: One of the toughest challenges in medical diagnosis is uncertainty handling. The detection of intestinal bacteria such as Salmonella and Shigella which cause typhoid fever and dysentery, respectively, is one such challenging problem for microbiologists. They detect the bacteria by the comparison with predefined classes to find the most similar one. Consequently, we observe uncertainty in determining the similarity degrees, and therefore, in the bacteria classification. In this paper, we take an intelligent approach towards the bacteria classification problem by using five similarity measures of fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) to examine their capabilities in encountering uncertainty in the medical pattern recognition. Methods: FSs and IFSs are two strong frameworks for uncertainty handling. The membership degree in FSs and both membership and non-membership degrees in IFSs are the operators that these frameworks use to represent the degree of which a member of the universe of discourse belongs to a subset of it. In this paper, the similarity measures, which both frameworks provide are used, so as the intestinal bacteria are detected and classified through uncertainty quantification in feature vectors. Also, the experimental results of using the measures are illustrated and compared. Results: We obtained 263 unknown bacteria from microbiology section of Resalat laboratory in Tehran to examine the similarity measures in practice. Finally, the detection rates of the measures were calculated between which IFS Hausdorf and Mitchel similarity measures scored the best results with 95.27% and 94.48% detection rates, respectively. On the other hand, FS Euclidean distance yielded only 85% detection rate. Conclusions: Our investigation shows that both frameworks have powerful capabilities to cope with the uncertainty in the medical pattern recognition problems. But, IFSs yield better detection rate as a result of more accurate modeling which is involved with incurring more computational cost. Our research also shows that among different IFS similarity measures, IFS Hausdorf and Mitchel ones score the best results.