Coronary heart disease risk assessment using Dempster-Shafer theory

2009 14th International CSI Computer Conference, CSICC 2009

Volumn , Issue , 2009, Pages 361-366

  Khatibi, Vahid ^a   Montazer, Gholam Ali ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY RATE; BELIEF AND PLAUSIBILITY; COMBINATION RULES; CORONARY HEART DISEASE; DEMPSTER-SHAFER THEORY; DEMPSTER-SHAFER THEORY OF EVIDENCE; FUZZY INFERENCE RULES; FUZZY RULE SET; FUZZY SETS THEORY; HYBRID ENGINES; RISK PREDICTIONS; SECOND PHASE; TWO PHASIS;

CARDIOLOGY; FORMAL LOGIC; FUZZY INFERENCE; FUZZY SETS; HEART; RISK ASSESSMENT; RISK MANAGEMENT;

FUZZY SET THEORY;

EID: 74549189087     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CSICC.2009.5349607     Document Type: Conference Paper

References (19)

1. S. K. De, A. Biswas, and R. Roy, "An application of intuitionistic fuzzy sets in medical diagnosis," Fuzzy Sets and Systems, vol. 117, pp. 209-213, 2001.
2. J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Mateo, CA: Elsevier, Morgan Kaufmann, 2006.
3. E. Straszecka, "Combining uncertainty and imprecision in models of medical diagnosis," Information Sciences, vol. 176, pp. 3026-3059, 2007.
4. L. A. Zadeh, "Fuzzy sets," Inform. Control, vol. 8, pp. 338-353, 1965.
5. J. W. Guan and D. A. Bell, Evidence Theory and its applications, 1st ed. vol. 1: Elsevier Science Publisher B.V., 1991.
6. G. Shafer, A mathematical theory of evidence. Princeton, NJ: Princeton Univ. Press, 1976.
7. A. P. Dempster, "Upper and lower probabilities induced by a multivalued mapping," Annals of Mathematical Statistics AMS, vol. 38, pp. 325-339, 1967.
8. J. W. Guan and D. A. Bell, Evidence Theory and its applications. Amsterdam: Elsevier Science Publisher B.V., 1991.
9. WHO, "Heart Diseases Report," World Health Organization 2008.
10. IHM, "Iranian Population Heart Disease Report," Ministry of Health and Medical Education 1387.
11. S. Simon, The Heart: Our Circulatory System: Morrow Junior, 1996.
12. M. D. McGoon, Mayo Clinic Heart Book, 2nd ed.: William Morrow, 2000.
13. C. McMahan, S. Gidding, Z. Fayad, A. Zieske, G. Malcom, R. Tracy, J. Strong, and H. J. McGill, "Risk scores predict atherosclerotic lesions in young people," Arch Intern Med, vol. 165, pp. 883-890, 2005.
14. P. Wilson, R. D'Agostino, D. Levy, A. Belanger, H. Silbershatz, and W. Kannel, "Prediction of coronary heart disease using risk factor categories," Circulation, vol. 97, pp. 1837-1847, 1998.
15. G. Assmann, P. Cullen, and H. Schulte, "Simple Scoring Scheme for Calculating the Risk of Acute Coronary Events Based on the 10-Year Follow-Up of the Prospective Cardiovascular Münster (PROCAM) Study," Circulation, vol. 105, pp. 310-315, 2002.
16. R. Detrano, "Machine Learning Repository; Heart Disease Dataset," V.A. Medical Center, Long Beach and Cleveland Clinic Foundation, 1988.
17. M. Kukar, I. Kononenko, C. Groselj, K. Kralj, and J. Fettich, "Analysing and improving the diagnosis of ischaemic heart disease with machine learning," Artificial Intelligence in Medicine, vol. 16, pp. 25-50, 1999.
18. M. Akay, "Noninvasive diagnosis of coronary artery disease using a neural network algorithm," Biological Cybernetics, vol. 67, pp. 361-367, 1992.
19. M. Haddad, K.-P. Adlassnigb, and G. Porenta, "Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams," Artificial Intelligence in Medicine, vol. 9, pp. 61-78, 1999.