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JACIII Vol.11 No.1 pp. 4-10
doi: 10.20965/jaciii.2007.p0004
(2007)

Paper:

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Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine

Cao Thang*, Eric W. Cooper**, Yukinobu Hoshino***,
and Katsuari Kamei**

*Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

**College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

***Dept. of Electronic and Photonic System Engineering, Kochi University of Technology, 185 Miyanoguchi, Tosayamada, Kami, Kochi 782-8502, Japan

Received:
October 31, 2005
Accepted:
March 31, 2006
Published:
January 20, 2007
Creative Commons License
Keywords:
neural network, fuzzy inference, oriental medicine
Abstract
In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of rheumatic states, its explanations and herbal prescriptions. First, an outline of the proposed decision support system is described after considering rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic state in which the patient appears to be infected, then it gives a prescription written in suitable herbs with reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally, we describe evaluations and restrictions of RETS.
Cite this article as:
C. Thang, E. Cooper, Y. Hoshino, and K. Kamei, “Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.1, pp. 4-10, 2007.
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References
  1. [1] L. A. Ba, “Treating reality rheumatism,” J. of Medicine and Pharmacy HCMC, June, 2001.
  2. [2] F. A. Maysam, D. G. von Keyserlingk, D. A. Linkens, and M. Mahfouf, “Survey of utilization of fuzzy technology in Medicine and Healthcare,” Fuzzy Sets and Systems, Vol.120, pp. 331-349, 2001.
  3. [3] M. B. Serrano, C. Sierra, and R. Lopez de Mantaras, “RENOIR: an expert system using fuzzy logic for rheumatology diagnosis,” Internat. J. of Intell. Systems, Vol.9, No.11, pp. 985-1000, 1994.
  4. [4] K. Boegl, F. Kainberger, K. P. Adlassnig, G. Kolousek, H. Leitich, G. Kolarz, and H. Imhof, “New approaches to computer-assisted diagnosis of rheumatologic diseases,” Radiologe, Vol.35, No.9, pp. 604-610, 1995.
  5. [5] E. H. ShortliEe, “Computer-Based Medical Consultations, MYCIN,” Elsevier, NY, 1976.
  6. [6] R. Montironi, P. H. Bartels, P. W. Hamilton, and D. Thompson, “A typical adenomatous hyperplasia (adenosis) of the prostate: development of a bayesian belief network for its distinction from well differentiated adenocarcinoma,” Hum. Pathol., Vol.27, No.4, pp. 396-407, 1996.
  7. [7] R. J. Guo, B. R. Ma, M. Xie, and W. L. Chen, “Fuzzy set model and computerised diagnosis system in traditional Chinese medicine,” Chinese J. of Comput., Vol.4, No.4, pp. 260-266, 1981.
  8. [8] H. P. Nguyen, “Approximate reasoning for oriental traditional medical expert systems,” IEEE Internat. Conf. on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, 4, NY, pp. 3084-3089, 1997.
  9. [9] R. Schmidt, B. Pollwein, L. Filipovici, and L. Gierl, “Adaptation and abstraction as steps towards case-based reasoning in the real medical world: Case-based selection strategies for antibiotics therapy,” Proc. of MEDINFO’95, North-Holland, Amsterdam, pp. 947-951, 1995.
  10. [10] H. P. Nguyen, B. T. Nguyen, and A. Ohsato, “Develop Case-based Reasoning for Medical Consultation Using the Importance of Features,” J. of Advanced Computational Intelligence, Vol.6, No.1, pp. 41-50, 2002.
  11. [11] H. P. Nguyen, “Toward Intelligent Systems for Integrated Western and Eastern Medicines,” TheGioi Publisher, Hanoi, 1997.
  12. [12] H. P. Nguyen, S. Pratit, and K. Hirota, “Fuzzy Modeling for Modifying Standard Prescriptions of Oriental Traditional Medicine,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.3, pp. 339-347, 2003.
  13. [13] R. Dybowski and V. Gant, “Clinical applications of artificial neural networks,” Cambridge University Press, 2001.
  14. [14] A. Bezerianos, S. Papadimitriou, and D. Alexopoulos, “Radial basis function neural networks for the characterization of heart rate variability dynamics,” J. of Artificial Intelligence in Medicine, Vol.15, No.3, pp. 215-234, 1999.
  15. [15] T. Thuy, P. D. Nhac, and H. B. Chau, “Lectures in Oriental Medicine,” Medicine Pub. Hanoi, Vol.2, pp. 160-165, 2002.
  16. [16] M. Negnevitsky, “Artificial Itelligence – A Guide to Intelligent Systems,” Pearson Education Limited, 2002.
  17. [17] J. Durkin, “Expert System, Design and Development,” Prentice Hall Inc, NY, 1994.

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