Combined numerical and linguistic knowledge representation and its application to medical diagnosis

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans.

Volumn 33, Issue 2, 2003, Pages 206-222

  Meesad, Phayung ^a   Yen, Gary G ^b  

^a KING MONGKUT'S UNIVERSITY OF TECHNOLOGY NORTH BANGKOK   (Thailand)

^b School of Electrical and Computer Engineering   (United States)

Author keywords

Decision support system; Fuzzy expert system; Genetic algorithms; Hybrid intelligent system; ILFN; Medical diagnosis; Pattern classification; Wisconsin breast cancer database

Indexed keywords

DECISION SUPPORT SYSTEMS; FUZZY SETS; GENETIC ALGORITHMS; INTELLIGENT CONTROL; LINGUISTICS;

HYBRID INTELLIGENT SYSTEMS;

DIAGNOSIS;

EID: 0041376933     PISSN: 10834427     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCA.2003.811290     Document Type: Article

References (51)

1. L. P. Seka, A. Fresnel, D. Delamarre, C. Riou, A. Burgun, B. Pouliquen, R. Duvauferrier, and P. Le Beux, "Computer assisted medical diagnosis using the web," Int. J. Med. Inform., vol. 47, no. 1-2, pp. 51-56, 1997.
2. F. M. H. M. Dupuits, A. Hasman, and P. Pop, "Computer-based assistance in family medicine," Comput. Methods Programs Biomed., vol. 55, no. 1, pp. 39-50, 1998.
3. L. Makris, I. Kamilatos, E. V. Kopsacheilis, and M. G. Strintzis, "Teleworks: A CSCW application for remote medical diagnosis support and teleconsultation," IEEE Trans. Inform. Technol. Biomed., vol. 2, pp. 62-73, June 1998.
4. D. Conforti and L. D. Luca, "Computer implementation of a medical diagnosis problem by patterns classification," Future Gener. Comput. Syst.s, vol. 15, no. 2, pp. 287-292, 1999.
5. D. J. Foran, D. Comaniciu, P. Meer, and L. A. Goodell, "Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy," IEEE Trans. Inform. Technol. Biomed., vol. 4, pp. 265-273, Dec. 2000.
6. K. P. Adlassnig, "The section on medical expert and knowledge-based systems at the department of medical computer sciences of the university of Vienna medical school," Artif. Intell. Med., vol. 21, no. 1-3, pp. 139-146, 2001.
7. G. P. K. Economou, D. Lymberopoulos, E. Karvatselou, and C. Chassomeris, "A new concept toward computer-aided medical diagnosis - A prototype implementation addressing pulmonary diseases," IEEE Trans. Inform. Technol. Biomed., vol. 5, pp. 55-66, Mar. 2001.
8. B. G. Buchana and E. H. Shortcliffe, Rule-Based Expert Systems: The MYCIN Experiment of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley, 1984.
9. W. A. J. J. Wiegerinck, H. J. Kappen, E. W. M. T. ter Break, W. J. P. P. ter Burg, M. J. Nijman, Y. L. O., and J. P. Neijt, "Approximate inference for medical diagnosis," Pattern Recognit. Lett., vol. 20, no. 11-13, pp. 1231-1239, 1999.
10. B. Kovalerchuk, E. Vityaev, and J. F. Ruiz, "Consistent knowledge discovery in medical diagnosis," IEEE Eng. Med. Biol., vol. 19, pp. 26-37, July/Aug. 2000.
11. L. Yan and D. J. Miller, "General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle," IEEE Trans. Neural Networks, vol. 11, pp. 558-573, May 2000.
12. C. A. Peña-Reyes and M. Sipper, "Designing breast cancer diagnostic via a hybrid fuzzy-genetic methodology," Proc. IEEE Int. Fuzzy Systems Conf., pp. 135-139, 1999.
13. D. Walter and C. K. Mohan, "ClaDia: A Fuzzy classifier system for disease diagnosis," in Proc. Congresss Evolutionary Computation, La Jolla, CA, 2000, pp. 1429-1435.
14. S. Zahan, "A fuzzy approach to computer-assisted myocardial ischemia diagnosis," Artif. Intell. Med., vol. 21, no. 1-3, pp. 271-275, 2001.
15. N. Belacel, P. Vincke, J. M. Scheiff, and M. R. Boulassel, "Acute leukemia diagnosis aid using multicriteria fuzzy assignment methodology," Comput. Methods Programs Biomed., vol. 64, no. 2, pp. 145-151, 2001.
16. A. Durg, W. V. Stoecker, J. P. Cookson, S. E. Umbaugh, and R. H. Moss, "Identification of variegated coloring in skin tumors: Neural network vs. rule-based induction methods," IEEE Eng. Med. Biology Mag., vol. 12, pp. 71-74, 98, Sept. 1993.
17. C. S. Pattichis, C. N. Schizas, and L. T. Middleton, "Neural network models in EMG diagnosis," IEEE Trans. Biomed. Eng., vol. 42, pp. 486-496, May 1995.
18. X. Yao and Y. Liu, "A new evolutionary system for evolving artificial neural networks," IEEE Trans. Neural Networks, vol. 8, pp. 694-713, May 1997.
19. D. West and V. West, "Model selection for a medical diagnostic decision support system: A breast cancer detection case," Artif. Intell. Med., vol. 20, no. 3, pp. 183-204, 2000.
20. V. Podgorelec, P. Kokol, and J. Zavrsnik, "Medical diagnosis prediction using genetic programming," Proc. 12th IEEE Symp. Computer-Based Medical Systems, pp. 202-207, 1999.
21. L. W. Man, L. Wai, S. L. Kwong, S. N. Po, and J. C. Y. Cheng, "Discovering knowledge from medical databases using evolutionary algorithms," IEEE Eng. Med. Biol., vol. 19, pp. 45-55, July/Aug. 2000.
22. C. A. Peña-Reyes and M. Sipper, "Evolutionary computation in medicine: An overview," Artif. Intell. Med., vol. 19, no. 1, pp. 1-23, 2000.
23. R. Setiono and H. Liu, "Symbolic representation of neural networks," IEEE Comput., vol. 29, pp. 71-77, Mar. 1996.
24. A. H. Tan, "Cascade ARTMAP: Integrating neural computation and symbolic knowledge processing," IEEE Trans. Neural Networks, vol. 8, pp. 237-250, Mar. 1997.
25. A. B. Tickle, R. Andrews, M. Golea, and J. Diederich, "The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks," IEEE Trans. Neural Networks, vol. 9, pp. 1057-1068, Nov. 1998.
26. I. A. Taha and J. Ghosh, "Symbolic interpretation of artificial neural networks," IEEE Trans. Knowl. Data Eng., vol. 11, pp. 448-463, May 1999.
27. P. Tino and M. Koteles, "Extracting finite-state representations from recurrent neural networks trained on chaotic symbolic sequences," IEEE Trans. Neural Networks, vol. 10, pp. 284-302, Mar. 1999.
28. S. Mitra and Y. Hayashi, "Neuro-Fuzzy rule generation: Survey in soft computing framework," IEEE Trans. Neural Networks, vol. 11, pp. 748-768, May 2000.
29. R. Setiono, "Generating concise and accurate classification rules for breast cancer diagnosis," Artif. Intell. Med., vol. 18, no. 3, pp. 205-219, 2000.
30. Y. Hayashi, "A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders," Artif. Intell. Med., vol. 20, no. 3, pp. 205-216, 2000.
31. S. I. Gallant, Neural Network Learning and Expert Systems. Cambridge, MA: MIT Press, 1993.
32. L. R. Medsker, Hybrid Neural Network and Expert Systems. Norwell, MA: Kluwer, 1994.
33. S. Goonatilake and S. Khebbal, Intelligent Hybrid Systems. New York: Wiley, 1995.
34. S. Wermter and R. Sun, "An overview of hybrid neural system," in Hybrid Neural Systems, S. Wermter and R. Sun, Eds. Berlin, Germany: Springer-Verlag, 2000, pp. 1-13.
35. L. M. Fu, H. H. Hsu, and J. C. Principe, "Incremental backpropagation learning networks," IEEE Trans. Neural Networks, vol. 7, pp. 757-761, May 1996.
36. G. A. Carpenter, S. Grossberg, N. Markuzon, J. H. Reynolds, and D. B. Rosen, "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps," IEEE Trans. Neural Networks, vol. 3, pp. 698-713, Sept. 1992.
37. G. Yen and P. Meesad, "Pattern classification by an incremental learning fuzzy neural network," in Proc. Int. Joint Conf. Neural Networks, Washington D.C, 1999, pp. 3230-3235.
38. _, "An effective neuro-fuzzy paradigm for machinery condition health monitoring," IEEE Trans. Syst., Man, Cybern. B, vol. 31, pp. 523-536, Aug. 2001.
39. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. SMC-15, pp. 116-132, 1985.
40. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: Univ. Michigan Press, 1975.
41. L. A. Zadeh, "Fuzzy sets," Inf. Control, vol. 8, pp. 338-353, 1965.
42. Y. Jin, "Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement," IEEE Trans. Fuzzy Syst., vol. 8, pp. 212-221, Apr. 2000.
43. M. Setnes, R. Babuska, and H. B. Verbruggen, "Rule-based modeling: Precision and transparency," IEEE Trans. Syst., Man, Cybern. C, vol. 28, pp. 165-169, Feb. 1998.
44. H. Ishibuchi, T. Nakashima, and T. Murata, "Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems," IEEE Trans. Syst., Man, Cybern. B, vol. 29, pp. 601-618, Oct. 1999.
45. 3 fuzzy rule systems from data using evolution strategies," IEEE Trans. Syst., Man, Cybern. B, vol. 29, pp. 829-845, Dec. 1999.
46. O. Cordon and F. Herrera, "A proposal for improving the accuracy of linguistic modeling," IEEE Trans. Fuzzy Syst., vol. 8, pp. 335-344, 2000.
47. L. X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst., Man, Cybern., vol. 22; pp. 1414-1427, Dec. 1992.
48. M. Seines, R. Babuska, U. Kaymak, and H. R. van nauta Lemke, "Similarity measures in fuzzy rule base simplification," IEEE Trans. Syst., Man, Cybern. B, vol. 28, pp. 376-386, 1998.
49. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989.
50. W. H. Wolberg and O. L. Mangasarian, "Multi-surface method of pattern separation for medical diagnosis applied to breast cytology," in Proc. Nat. Acad. Sci., vol. 87, 1990, pp. 9193-9196.
51. C. L. Blake and C. J. Merz. (1998) UCI Repository of Machine Learning Databases. Univ. California, Irvine, CA. [Online]. Available: http://www.ics.uci.edũmlearn/MLRepository.html