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

Conference paper

DOI: 10.1007/978-3-319-59162-9_7

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)
Cite this paper as:
Porebski S., Straszecka E. (2018) Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements. In: Kurzynski M., Wozniak M., Burduk R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham

Abstract

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.

Keywords

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

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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