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.
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References
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)
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)
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)
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)
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)
Porebski, S., Straszecka, E.: Membership functions for fuzzy focal elements. Arch. Control Sci. 26(3), 281–313 (2016)
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)
Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Inf. Sci. 176, 3026–3059 (2006)
UCI Machine Learning Repository: Thyroid Disease Data Set. https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease. Accessed 22 Dec 2016
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This research is financed from the statutory activities of the Institute of Electronics of the Silesian University of Technology.
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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. https://doi.org/10.1007/978-3-319-59162-9_7
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DOI: https://doi.org/10.1007/978-3-319-59162-9_7
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