Recognition Patterns Construction of Coronary Heart Disease Patients with Qi Stagnation Syndrome Based on Decision Tree

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Coronary heart disease (CHD), called Thoracic Obstruction in TCM, is one of the most important types of heart disease for its high incidence and high mortality. The methods of syndrome studies in TCM can not be completely in accordance with that of modern medicine because of the complexity itself. In this paper, we decide to investigate the ability of Decision Tree to predict CHD patients with or without qi stagnation syndrome. Predictions with CHAID Decision Tree (one type of the Decision Trees), we obtained recognition patterns made up of seven biological parameters. The accuracy of this diagnosis pattern was 80.7%, the sensitivity and specificity could reach 72.1% and 83.0%. The ADTree recognition pattern include six biological indicators. The accuracy of this diagnosis pattern was 94.6%, the sensitivity and specificity could reach 100% and 94.3%.

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1025-1031

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December 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] A. P Lv, S. Li, Y. Y Wang: JTCM. Vol. 46(2005), pp.5-7.

Google Scholar

[2] H. H Zhao, S. W Guo, J. X Chen, et al.: Bioinformatics and Biomedical Engineering(iCBBE), 2010 4th International Conference on: 1-3.

Google Scholar

[3] H. H Zhao, J. X Chen, N. Hou, et al.: Evidence- based Compl. and Alt. Medicine. Vol. 7(2010), pp.101-108.

Google Scholar

[4] M. Qu, M. X Zhang, L. Zhang, et al.: Chinese Archives Of Traditional Chinese Medicine. Vol. 28(2010), pp.282-285.

Google Scholar

[5] H. H Zhao, W. Wang.: Acta Chimica Sinica. Vol. 67(2009), pp.167-173.

Google Scholar

[6] H. H Zhao, N. Hou, W. Wang.: Spectroscopy and Spectral Analysis. Vol. 29(2009), pp.1647-1650.

Google Scholar

[7] H. H Zhao, J. X Chen, N. Hou, et al.: Evidence-based Compl. and Alt. Medicine. Vol. 7(2010), pp.101-18.

Google Scholar

[8] H. H Zhao, N. Hou, W. Wang, et al.: Chinese Journal of Integrated Traditional and Western Medicine. Vol. 29(2009), pp.489-492.

Google Scholar

[9] H. H Zhao, F. Yang, W. Wang, et al.: Chemical Journal of Chinese Universities. Vol. 31(2010), pp.285-292.

Google Scholar

[10] C. H Shi, H. H Zhao, N. Hou, et al.: Chemical Research in Chinese Universites. Vol. 27(2011), pp.87-93.

Google Scholar

[11] G. Qi Qi, Y. Y Li, P. Zhang, et al.: Chinese Journal Of Traditional Chinese Medicine. Vol. 26(2011), pp.380-383.

Google Scholar

[12] H. H Zhao, S. W Guo, J. X Chen, et al.: Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th International Conference on, 1-3.

Google Scholar

[13] Ian H. Witten, Eibe Frank. Data Mining Practical Machine Learning Tools and Techniques. Beijing: China Machine, press, (2005).

Google Scholar

[14] Peters RP, Twisk JW, van AgtmaeIMA, et al.: Clin Microbiol Infect. Vol. 12(2006), pp.1207-1213.

Google Scholar

[15] F. X.: Computer Knowledge and Technology. Vol. 6( 2010), pp.2493-2495.

Google Scholar

[16] L. Y Luo, J. X Chen.: Medicine Information. Vol. 21(2008), p.1936-(1939).

Google Scholar

[17] L. Huang, J. M Yuan, A. H Ou, et al. The Journal of Practical Medicine. Vol. 27(2011), pp.121-124.

Google Scholar

[18] X. F Liang, J. F Liu, L. Gao, et al.: Practical Preventive Medicine. Vol. 17( 2010), p.1938-(1940).

Google Scholar

[19] Y. R Lu, X. Y Feng, Z. H Liang.: Chinese Journal of Health Statistics. Vol. 27(2010), pp.572-576.

Google Scholar

[20] W. B Li, Y. N Zhou, Q. L.: China Medical Herald. Vol. 7(2010), pp.110-113.

Google Scholar

[21] X. Pan.: Clinical Engineering. Vol. 14( 2008), pp.81-83.

Google Scholar

[22] L. N Wang, S. M Pan.: Chin J Evid-based Med. Vol. 11( 2011), pp.591-593.

Google Scholar

[23] Kass G.V.: Applied Statistics. Vol. 29(1980), pp.119-127.

Google Scholar

[24] G. Z Wang.: Market & Demographic Analysis. Vol. 5(1999), pp.16-19.

Google Scholar

[25] L. Shi, Y. Wang.: Chinese Journal of Health Statistics. Vol. 19(2002), pp.283-285.

Google Scholar

[26] Brigham A, Andrew M. ADtrees for Fast Counting and for Fast Learning of Association Rules. In Proceedings Fourth International Conference on Knowledge Discovery and Data Mining, (1998).

Google Scholar