Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


The Dempster-Shafer theory along with the fuzzy set theory are suitable tools for the medical diagnosis support. They can deal with medical knowledge uncertainty and data imprecision. This paper presents a study of medical knowledge representation by means of the Dempster-Shafer theory extended with the fuzzy set theory and introduces the new rule selection algorithm. The presented method gives an opportunity of interpretable and reliable rule extraction. The method is elaborated and its performance is tested on a popular medical data set. Results show that the presented method can be useful for the knowledge engineer and diagnostician cooperation due to the simple rule base and clear inference method.


Rule extraction Dempster-Shafer theory Fuzzy sets Medical diagnosis support Thyroid disease 



This research is financed from the statutory activities of the Institute of Electronics of the Silesian University of Technology.


  1. 1.
    Amato, F., López, A., Pena-Meñdez, E.M., Vaňhara, P., Hampl, A., Havel, J.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11(2), 47–58 (2013)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181, 4340–4360 (2011)CrossRefGoogle Scholar
  4. 4.
    Han, L., Luo, S., Yu, J., Pan, J., Chen, S.: Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J. Biomed. Health Inform. 19(2), 728–734 (2015)CrossRefGoogle Scholar
  5. 5.
    Liu, X.D., Feng, X., Pedrycz, W.: Extraction of fuzzy rules from fuzzy decision trees: an axiomatic fuzzy sets (afs) approach. Data Knowl. Eng. 84, 1–25 (2013)CrossRefGoogle Scholar
  6. 6.
    Porebski, S., Straszecka, E.: Membership functions for fuzzy focal elements. Arch. Control Sci. 26(3), 281–313 (2016)Google Scholar
  7. 7.
    Straszecka, E., Straszecka, J.: Interpretation of Medical Symptoms Using Fuzzy Focal Elements. In: Kurzyński M., Puchała E., WoŹniak M., żołierek A. (eds) Computer Recognition Systems: Proceedings of the 4th International Conference on Computer Recognition Systems CORES 2005. Advances in Soft Computing, vol 30, pp. 287–293. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Inf. Sci. 176, 3026–3059 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    UCI Machine Learning Repository: Thyroid Disease Data Set. https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease. Accessed 22 Dec 2016

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

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