Abstract
Traditional Chinese Medicine (TCM) is one of the most important complementary and alternative medicines. Due to the subjectivity and fuzziness of diagnosis in TCM, quantitative model or methods are needed to facilitate the popularization of TCM. In this article, a novel quantitative method for syndrome differentiation based on BNs is proposed. First the symptoms are selected by a novel mutual information based symptom selection algorithm (MISS) and then the mapping relationships between the selected symptoms and key elements are constructed. Finally, the corresponding syndromes are output by combining the key elements. The results show that the diagnostic model obtains relative reliable predictions of syndrome, and its average predictive accuracy rate reach 91.68%, which testifies that the method we proposed is feasible and effective and can be expected to be useful in the modernization of TCM.
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References
Ma, B.: Expert systems and knowledge bases in traditional Chinese medicine (In Chinese). Beijing Press, Beijing (1994)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Francisco (1988)
Neapolitan, R.E.: Probabilistic Reasoning in Expert Systems. John Wiley and Sons, New York (1990)
Dubois, D., Prade, H., Yager, R.R.: Fuzzy information engineering: A guide tour of applications, pp. 149–162. Wiley, New York (1997)
Cheng, J., Hatzis, C., Hayashi, H., Krogel, M.-A., Morishita, S., Page, D., Sese, J.: KDD Cup 2001 report. SIGKDD Explor. Newsl. 2002, 3(2), 47–64 (2001)
Wang, X.W., Qu, H.B., Liu, P., Cheng, Y.Y.: A self-learning expert systems for diagnosis in traditional Chinese medicine. Expert Systems with Applications 26, 557–566 (2004)
Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B 50, 157–224 (1988)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. JMLR 3, 1289–1306 (2003)
Wong, A.K.C., Chiu, D.K.Y.: Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. 9(6), 796–805 (1987)
Mullin, M., Sukthankar, R.: Complete cross-validation for nearest neighbor classifiers. In: Proceedings of ICML, pp. 639–646 (2000)
Zhu, W.F.: The establishment of united system of syndrome differentiation. Chinese Journal of Basic Medicine in Traditional Chinese Medicine 4, 4–6 (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, H., Wang, J. (2006). A Quantitative Diagnostic Method Based on Bayesian Networks in Traditional Chinese Medicine. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_20
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DOI: https://doi.org/10.1007/11893295_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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