The basic probability assignment as a measure of diagnostic rules significance

• Autorzy: Straszecka E.
• Języki publikacji: EN

Abstrakty

EN

Diagnostic rules are usually IF-THEN rules, but they should satisfy specific requirements of a diagnosis. Thus, not always the classical methods of rules determination are applicable. In the present paper it is suggested to find out the set of rules by an elimination of superfluous rules from the maximal rule set or adding rules that improve inference to the minimal set of rules. It is shown that the basic probability assignment determined in the Dempster-Shafer theory of evidence can be used as a measure indicating symptoms that are the most significant for a diagnosis and should create rules. A set of IF-THEN rules with fuzzy premises and crisp conclusions can be built in this way. The proposed method is illustrated by determining rules allowing for diagnostic inference for a database of thyroid gland diseases.

Słowa kluczowe

EN

diagnostic rules; fuzzy sets; Dempster-Shafer theory

PL

diagnostyka; zbiory rozmyte; teoria Dempstera-Shafera

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2013
• Tom: Vol. 22
• Strony: 95--102
• Opis fizyczny: Bibliogr. 8 poz., rys., wykr.

Twórcy

autor

Straszecka E.

• Institute of Electronics, Silesian University of Technology, 16 Akademicka St., 44-100 Gliwice

Bibliografia

• [1] DEMPSTER A. P., A generalization of Bayesian inference, J. Royal Stat. Soc., J. Wiley, New Jersey, USA, 1968, Vol. 30, No. 2, pp. 205-247.
• [2] KACPRZYK J., FEDRIZZI M. (eds.), Advances in Dempster-Shafer Theory of Evidence, J. Wiley, New York, USA, 1994.
• [3] LUCAS P. J. F., Model-based diagnosis in medicine, Artificial Intelligence in Medicine, Elsevier, 1997, Vol. 10, pp. 201-208.
• [4] SCHORK M. A., REMINGTON R. D., Statistics with Applications to the Biological and Health Sciences, Prentice Hall Inc., Upper Saddle River, New Jersey, USA, 2000.
• [5] STRASZECKA E., Combining uncertainty and imprecision in models of medical diagnosis, Information Sciences, Elsevier, 2006, Vol. 176, pp. 3026-3059.
• [6] STRASZECKA E., Measures of uncertainty and imprecision in medical diagnosis support, Wyd. Pol. Sl., Gliwice, 2010.
• [7] STRASZECKA E., Uncertainty and imprecision in medical diagnosis support, J. Medical Informatics & Technologies, Univ. of Silesia, Katowice, Poland, 2012, Vol. 19, pp. 11-22.
• [8] database online: ftp.ics.uci.edu/pub/machine-learning-databases/thyroid-disease, files new-thyr.*, 5.06.2013