Advertisement

Using Fuzzy Numbers for Modeling Series of Medical Measurements in a Diagnosis Support Based on the Dempster-Shafer Theory

  • Sebastian PorebskiEmail author
  • Ewa Straszecka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

This work concern attempts to model imprecise symptoms in the medical diagnosis support tools. Patient’s self-check is very important, particularly in chronic diseases. In hypertension or diabetes patients record measurements. Still, these measurements are made in different circumstances, thus they are imprecise. A physician takes into account rather a trend in a series of measurements to diagnose a patient. Till now, knowledge engineers’ approach is different since they often use a single value as input information of a decision support system. In this work, a series of measurements is modeled as a fuzzy number. The main purpose of the presented approach is to check whether it is possible to replace a single measurement with a series of measurements in the diagnosis support system and to examine the impact of this change on the diagnosis process. Preliminary results show that use of the fuzzy number in determining the diagnosis may increase its certainty and can be profitable when used in real medical problems.

Keywords

Medical diagnosis support Series of measurements Imprecise information Dempster-Shafer theory Fuzzy numbers 

Notes

Acknowledgements

This research is financed from the statutory funds (BKM-510/Rau-3/2017 & BK-232/Rau-3/2017) of the Institute of Electronics of the Silesian University of Technology, Gliwice, Poland.

References

  1. 1.
    Casanovas, M., Merigo, J.M.: Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure. Expert Syst. Appl. 39(8), 7138–7149 (2012)CrossRefGoogle Scholar
  2. 2.
    Chai, K.C., Tay, K.M., Lim, C.P.: A new method to rank fuzzy numbers using Dempster-Shafer theory with fuzzy targets. Inf. Sci. 346, 302–317 (2016)CrossRefGoogle Scholar
  3. 3.
    Esfandiari, N., Babavalian, M.R., Moghadam, A.-M.E., Tabar, V.K.: Knowledge discovery in medicine: current issue and future trend. Expert Syst. Appl. 41(9), 4434–4463 (2014)CrossRefGoogle Scholar
  4. 4.
    Ghasemini, J., Ghaderi, R., Mollaei, M.R.K., Hojjatoleslami, S.A.: A novel fuzzy Dempster-Shafer inference system for brain MRI segmentation. Inf. Sci. 223, 205–220 (2013)CrossRefGoogle Scholar
  5. 5.
    Hwang, C.M.: Belief and plausibility functions on intuitionistic fuzzy sets. Int. J. Intell. Syst. 31(6), 556–568 (2016)CrossRefGoogle Scholar
  6. 6.
    Ishizuka, M.: Inference procedures under uncertainty for the problem-reduction method. Inf. Sci. 28(3), 179–206 (1982)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Jiang, W., Yang, W., Luo, Y., Qin, X.Y.: Determining basic probabilisty assignment based on the improved similarity measures of generalized fuzzy numbers. Int. J. Comput. Commun. Control 10(3), 333–347 (2015)CrossRefGoogle Scholar
  8. 8.
    Liao, H., Xu, Z., Zeng, X.-J., Merigo, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl. Based Syst. 76, 127–138 (2015)CrossRefGoogle Scholar
  9. 9.
    Ogawa, H., Fu, K.S., Yao, J.T.P.: An inexact inference for damage assessment of existing structures. Int. J. Man-Mach. Stud. 22(3), 295–306 (1985)CrossRefGoogle Scholar
  10. 10.
    Porebski, S., Straszecka, E.: Extracting easily interpreted diagnostic rules. Inf. Sci. 426, 19–37 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Porwik, P., Orczyk, T., Lewandowski, M., Cholewa, M.: Feature projection k-NN classifier model for imbalanced and incomplete medical data. Biocybern. Biomed. Eng. 36(4), 644–656 (2016)CrossRefGoogle Scholar
  12. 12.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, New Jersey (1976)zbMATHGoogle Scholar
  13. 13.
    Straszecka, E.: Combining knowledge from different sources. Expert Syst. 27(1), 40–52 (2010)CrossRefGoogle Scholar
  14. 14.
    Tang, H.: A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster-Shafer theory of evidence. Appl. Soft Comput. 31, 317–325 (2015)CrossRefGoogle Scholar
  15. 15.
    Wang, J., Hu, Y., Xiao, F., Deng, X., Deng, Y.: A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: an application in medical diagnosis. Artif. Intell. Med. 69, 1–11 (2016)CrossRefGoogle Scholar
  16. 16.
    Yager, R.R.: Generalized probabilities of fuzzy events from fuzzy belief structures. Inf. Sci. 28(192), 45–62 (1982)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Yager, R.R.: On the fusion of imprecise uncertainty measures using belief structures. Inf. Sci. 181(15), 3199–3209 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Automatic Control, Electronics and Computer Science, Institute of ElectronicsSilesian University of TechnologyGliwicePoland

Personalised recommendations