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Abstrakty
In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.
Czasopismo
Rocznik
Tom
Strony
115--133
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
- Department of Computer Science, University of Uyo, Uyo, Nigeria
autor
- Department of Computer Science and Information Systems, Mount Royal University, 4825 Mount Royal Gate SW, AB T3E 6K6, Calgary, Alberta, Canada
autor
- Department of Computer Science, Federal University of Technology, Akure, Nigeria
autor
- Department of Cardiology, University of Uyo Teaching Hospital, Uyo, Nigeria
Bibliografia
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- 2. Obot OU, Uzoka FM. A framework for application of neuro-case-rule base hybridization in medical diagnosis. Appl Soft Comput 2009:9:245-53.
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- 4. Song Q, Kasabov N. A novel generic higher-order TSK fuzzy model for prediction and applications for medical decision support. In: Proc. 8th Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), 10-12. December, 2003, Sydney, NSW, Australia, 2003:241-5.
- 5. Pople HE. Heuristic methods for imposing structure on ill-structured problems: the structuring of medical diagnostics. In: Szolovits P, editor. Artificial intelligence in medicine. Boulder, CO: Westview Press, 1982:chap 5.
- 6. Miller RA, Pople HE, Myers JD. INTERSIT-1: an experimental computer based diagnostic consultant for general internal medicine. N EnglJ Med 1982;316:250-8.
- 7. Podgorelec V, Kokol P. Towards more optimal medical diagnosing with evolutionary algorithms. J Med Syst 2001:25:195-219.
- 8. Ochi-Okorie AS. Disease diagnosis validation in TROPIX using CBR. Artif Intell Med 1998;12:43-60.
- 9. Timpka T, Padgham L, Hedblom P, Wallin S, Tibblin G. A hypertext knowledge base for primary care. ACM 1989;23: 221-228.
- 10. Uzoka F-ME, Famuyiwa FO. A framework for the application of knowledge technology to the management of diseases. Int J Health Care Qual Assur 2004;17:194-204.
- 11. Akinyokun OC, Adeniyi OA. Experimental study of intelligent computer aided medical diagnostics and therapy. AMSE J Model Simul Control 1991;27:l-20.
- 12. Bonnisone PP, Geobel K, Khedkar PS. Hybrid soft computing systems: industrial and commercial applications. Proc IEEE 1999:87:1641-67.
- 13. Lefebvre C, Principe J. Object-oriented artificial neural network implementations. World Congr Neural Networks 1993:4:436-9.
- 14. Lefebvre C, Principe J. NeuroSolution 6.0. Gainesville, FL: NeuroDimension, 2007.
- 15. Economou GP, Hallas JA, Mariatos EP, Goutis CE. Artificial networks in medical decision making systems: an application to pulmonary diseases' diagnosis through VHDL synthesis. Proc Eur Design Test Conf 1995:590.
- 16. Gomez-Ruiz JA, Jerez-Aragones JM, Munoz-Perez J. A neural network based model for prognosis of early breast cancer. Appl Intell 2004:20:231-8.
- 17. Daniels JE, Cayton RM. Cadosa: a fuzzy expert system for differential diagnosis of obstructive sleep apnoea and related conditions. Expert Syst Appl 1997;12:l63-77.
- 18. WainerJ, SandriS. Fuzzy temporal/categorical information in diagnosis. J Intell Inf Syst 1999;13:9-29.
- 19. Obot OU, Uzoka FM. Fuzzy rule-based framework for the management of tropical diseases IntJ Med Eng Inf 2008;l:7-17.
- 20. Marantz PR, Tobin JN, Watsertheil-Samaler S, Steingart RM, WexlerJP, Budner N. The relationship between left ventricular systolic function and congestive heart failure diagnosed by clinical criteria. Circulation 1988:77:607-12.
- 21. Obot OU, Akinyokun OC, Udoh SS. Application of soft computing methodologies to the management of hypertension. J Comput Appl 2008:15:131-47.
- 22. Anderson GR. Your guide to health. Pune, India: Oriental Watchman Publishing House, 1976.
- 23. Sutton GE, Fox KM. Heart diseases (pathological, clinical and investigatory features). Pune, India: Lippincot Company, 1990:43-47.
- 24. Braunwald E, Grossman W. Clinical aspects in heart failure. In: Braunwald E, editor. Heart disease: a textbook in cardiovascular medicine, 5th ed. 1997:445-70.
- 25. Davie AP, Francis CM, Caruana L, Sutherland GR, McMurray J J. Assessing diagnosis in heart failure: which features are any use? QJM 1997:90:335-9.
- 26. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, et al. ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in the adult. Circulation 2005:112:1825-52.
- 27. Hobbs R, Boyle A. Disease Management Project ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary: a report of the American College of Cardiology/American Heart Association, 2005. Available at: http://circ.ahajournals.org/content/112/12/1825. full.pdf+html?sid=d3454c8f-0fe5-4873-b4d7-459164dc8bel. Accessed on December 12, 2010.
- 28. Abraham A, Nath B. Evolutionary design of fuzzy control systems - a hybrid approach. In: 6th International Conference on Control, Automation, Robotics and Vision (ICARCV, 2000). 2000. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.1853&rep=repl&type=pdf. Accessed on April 15, 2007.
- 29. Takagi T, Sugeno M. Derivation of fuzzy control rules from human operators control actions. In: Proceedings of the IAFC Symposium on Fuzzy Information, Knowledge Representation nd Decision Analysis, 1993:55-60.
- 30. Shogbon JA. Neuro-fuzzy expert system for appraisal of capital investment, PhD thesis dissertation. Akure: Federal University of Technology, 2003.
- 31. Luneski A, Konstantinidis E, Bamidis PD. Affective medicine: a review of affective computing efforts in medical informatics. MIM 2010;49:207-18. Available at: http://www.schattauer.de/en/magazine/subject-areas/journals-a-z/methods/issue/special/manuscript/12987/show.html. Accessed on Septembers, 2012.
- 32. Uzoka FM, Osuji J, Obot 0. Clinical decision support system (DSS) in the diagnosis of malaria: a case comparison of two soft computing methodologies. Expert Syst Appl J 2011:38:1537-53.
Typ dokumentu
Bibliografia
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