On advantages of a fuzzy approach to a diagnosis support

Advances in Intelligent Systems and Computing

Volumn 323, Issue , 2015, Pages 345-355

  Straszecka, Ewa ^a  

^a SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Dempster shafer theory of evidence; Diagnosis Support; Fuzzy sets

Indexed keywords

FUZZY SETS;

BENCHMARK DATABASE; DEMPSTER-SHAFER THEORY; DEMPSTER-SHAFER THEORY OF EVIDENCE; DIAGNOSIS SUPPORT; DIAGNOSTIC PROBLEM; FUZZY INTERPRETATIONS; LABORATORY TEST; SIMULATION AND CALCULATIONS;

FORMAL LOGIC;

EID: 84921482511     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-11310-4_30     Document Type: Article

References (11)

1. Coomans, D., Broeckaert, I., Jonckheer, M., Massart, D.L.: Comparison of multivariate discrimination techniques for clinical data-application to the thyroid functional state. Methods of Information in Medicine 22, 93–101 (1983)
2. Dempster, A.P.: A generalization of Bayesian inference. Jornal of Royal Statistic Society 30(2), 205–247 (1968)
3. Innocent, P.R., John, R.I.: Computer aided fuzzy medical diagnosis. Information Sciences 162, 81–104 (2004)
4. Ledley, R.S.: Practical problems in the use of computers in medical diagnosis. Proceedings of the IEEE 57(11), 1900–1918 (1969)
5. Kacprzyk, J., Fedrizzi, M. (eds.): Advances in Dempster-Shafer Theory of Evidence. J. Wiley, New York (1994)
6. Liu, Z., Li, Y.: A new heuristic algorithm of rules generation based on rough sets. In: Proceedings of the International Seminar on Business and Information Management, pp. 291–294. IEEE (2008)
7. Pratap, A., Kanimozhiselvi, C.S.: Application of Naive Bayes Dichotomizer Supported with Expected Risk and Discriminant Functions in Clinical Decision - Case Study. In: 4th IEEE International Conference on Advanced Computing, pp. 1–4. IEEE Conference Publications (2012)
8. Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Information Sciences 176, 3026–3059 (2006)
9. Straszecka, E.: Measures of uncertainty and imprecision in medical diagnosis support. Wydawnictwo Politechniki Slaskiej, Gliwice (2010)
10. Straszecka E.: A choice of uncertainty and imprecision representation for diagnostic reasoning. In: Atanassov K., Homenda W., Hryniewicz O., Kacprzyk J., Krawczak M., Nahorski Z., Szmidt E., Zadrozny S. (eds.) New Trends in Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets and Related Topics, Vol. II: Applications, pp.161-179. IBS PAN, Warsaw (2013)
11. database online, ftp.ics.uci.edu/pub/machine-learning-databases/thyroid-disease, files new-thyr.* (June 5, 2013)