DOI QR코드

DOI QR Code

A Headache Diagnosis Method Using an Aggregate Operator

  • Ahn, Jeong-Yong (Department of Statistics (Institute of Applied Statistics), Chonbuk National University) ;
  • Choi, Kyung-Ho (Department of Basic Medical Science, Jeonju University) ;
  • Park, Jeong-Hyun (Department of Statistics, Chonbuk National University)
  • Received : 2011.12.08
  • Accepted : 2012.03.19
  • Published : 2012.05.31

Abstract

The fuzzy set framework has a number of properties that make it suitable to formulize uncertain information in medical diagnosis. This study introduces a fuzzy diagnostic method based on the interval-valued interview chart and the interval-valued intuitionistic fuzzy weighted arithmetic average(IIFWAA) operator. An issue in the use of the IIFWAA operator is to determine the weights. In this study, we propose the occurrence information of symptoms as the weights. An illustrative example is provided to demonstrate its practicality and effectiveness.

Keywords

References

  1. Adlassnig, K. P. (1986). Fuzzy set theory in medical diagnosis, IEEE Transactions on Systems, Man, and Cybernetics, 16, 260-265. https://doi.org/10.1109/TSMC.1986.4308946
  2. Ahn, J. Y., Han, K. S., Oh, S. Y. and Lee, C. D. (2011). An application of interval-valued intuitionistic fuzzy sets for medical diagnosis of headache, International Journal of Innovative Computing, Information and Control, 7, 2755-2762.
  3. Ahn, J. Y., Kim, Y. H. and Kim, S. K. (2003). A fuzzy differential diagnosis of headache applying linear regression method and fuzzy classification, IEICE Transactions on Information and Systems, E86-D, 2790-2793
  4. Ahn, J. Y., Mun, K. S., Kim, Y. H., Oh, S. Y. and Han, B. S. (2008). A fuzzy method for medical diagnosis of headache, IEICE Transactions on Information and Systems, E91-D, 1215-1217. https://doi.org/10.1093/ietisy/e91-d.4.1215
  5. Atanassov, K. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
  6. Atanassov, K. and Gargov, G. (1989). Interval-valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31, 343-349. https://doi.org/10.1016/0165-0114(89)90205-4
  7. Aversa, F., Gronda, E., Pizzuti, S. and Aragno, C. (2002). A fuzzy logic approach to decision support in medicine, Proceedings of the Conference on Systemics, Cybernetics and Informatics.
  8. Buckley, J. J. (2006). Fuzzy Probability and Statistics, Springer.
  9. Dubois, D., Gottwald, S., Hajek, P., Kacprzyk, J. and Prade, H. (2005). Terminological difficulties in fuzzy set theory - The case of Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 156, 485-491. https://doi.org/10.1016/j.fss.2005.06.001
  10. Innocent, P. R. and John, R. I. (2004). Computer aided fuzzy medical diagnosis, Information Sciences, 162, 81-104. https://doi.org/10.1016/j.ins.2004.03.003
  11. Kim, Y. H., Kim, S. K., Oh, S. Y. and Ahn, J. Y. (2007). A fuzzy differential diagnosis of headache, Journal of the Korean Data and Information Science Society, 18, 429-438.
  12. Kumar, S., Biswas, R. and Roy, A. R. (2001). An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets and Systems, 117, 209-213. https://doi.org/10.1016/S0165-0114(98)00235-8
  13. Park, J. H., Lim, K. M., Park, J. S. and Kwun, Y. C. (2008). Distances between interval-valued intuitionistic fuzzy sets, Journal of Physics: Conference Series, 96.
  14. Sanchez, E. (1979). Medical diagnosis and composite fuzzy relations, Gupta, M.M., Ragade, R.K., Yager R.R. (Eds.) Advances in Fuzzy Set Theory and Applications, 437-444.
  15. Seising, R. (2004). A history of medical diagnosis using fuzzy relations, Proceedings in the Conference on Fuzziness.
  16. Turksen, B. (1986). Interval valued fuzzy sets based on normal forms, Fuzzy Sets and Systems, 20, 191-210. https://doi.org/10.1016/0165-0114(86)90077-1
  17. Xu, Z. S. (2007). Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making, Control and Decision, 22, 215-219.
  18. Yamada, K. (2004). Diagnosis under compound effects and multiple causes by means of the conditional causal possibility approach, Fuzzy Sets and Systems, 145, 183-212. https://doi.org/10.1016/S0165-0114(03)00306-3
  19. Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  20. Zadeh, L. A. (1969). Biological applications of the theory of fuzzy sets and systems, Proceedings of an International Symposium on Biocybernetics of the Central Nervous System, 99-206.
  21. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning, Part I, Information Science, 8, 199-249. https://doi.org/10.1016/0020-0255(75)90036-5
  22. Zimmerman, H. J. (1991). Fuzzy Set Theory and its Application, Kluwer Academic Publishers.

Cited by

  1. A new medical diagnosis method based on Z-numbers 2017, https://doi.org/10.1007/s10489-017-1002-4