- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Evidence-Based Complementary and Alternative Medicine
Volume 2012 (2012), Article ID 142584, 11 pages
In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine
1Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2Beijing University of Chinese Medicine, 11 Bei San Huan Dong Lu, ChaoYang District, Beijing 100029, China
Received 11 November 2011; Revised 20 January 2012; Accepted 21 January 2012
Academic Editor: Hao Xu
Copyright © 2012 Peng Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- WHO; World Heart Federation, World Stroke Organization, Global Atlas on Cardiovascular Disease Prevention and Control, 2011.
- WHO, “Cardiovascular diseases (CVDs),” 2011.
- G. P. Liu, G. Z. Li, Y. L. Wang, and Y. Q. Wang, “Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning,” BMC Complementary and Alternative Medicine, p. 37, 2010.
- M. Jiuzhang and G. Lei, A General Introduction to Traditional Chinese Medicine, CRC Press, 2009.
- A. P. Lu, H. W. Jia, C. Xiao, and Q. P. Lu, “Theory of traditional chinese medicine and therapeutic method of diseases,” World Journal of Gastroenterology, vol. 10, no. 13, pp. 1854–1856, 2004.
- S. Li, Z. Q. Zhang, L. J. Wu, X. G. Zhang, Y. D. Li, and Y. Y. Wang, “Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network,” IET Systems Biology, vol. 1, no. 1, pp. 51–60, 2007.
- C. D. Gu, The Inner Classic of the Yellow Emperor, Essential Questions (Huangdi Neijing, Suwen), People’s Medical Publishing House, Beijing, China, 1956.
- Y. Wang, L. Ma, and P. Liu, “Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine,” Computer Methods and Programs in Biomedicine, vol. 95, no. 3, pp. 249–257, 2009.
- S. Lukman, Y. He, and S. C. Hui, “Computational methods for traditional Chinese medicine: a survey,” Computer Methods and Programs in Biomedicine, vol. 88, no. 3, pp. 283–294, 2007.
- W. Jie, W. Rong, and Z. Xuezhong, “Syndrome factors based on SVM from coronary heart disease treated by prominent TCM doctors,” Journal of Beijing University of Traditional Chinese Medicine, vol. 08, 2008.
- X. Zhou, S. Chen, B. Liu et al., “Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support,” Artificial Intelligence in Medicine, vol. 48, no. 2-3, pp. 139–152, 2010.
- J. Chen, G. Xi, J. Chen et al., “An unsupervised pattern (syndrome in traditional Chinese medicine) discovery algorithm based on association delineated by revised mutual information in chronic renal failure data,” Journal of Biological Systems, vol. 15, no. 4, pp. 435–451, 2007.
- B. Wang, M. W. Zhang, B. Zhang, and W. J. Wei, “Data mining application to syndrome differentiation in traditional Chinese medicine,” in Proceedings of the 7th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT '06), pp. 128–131, December 2006.
- H. L. Wu, C. Keji, X. M. Ruan, and W. J. Luo, “Cluster analysis on TCM syndromes in 319 coronary artery disease patients for establishment of syndrome diagnostic figure,” Chinese Journal of Integrated Traditional and Western Medicine, vol. 27, no. 7, pp. 616–618, 2007.
- X. J. Ma, H. J. Yin, and K. J. Chen, “Differential gene expression profiles in coronary heart disease patients of blood stasis syndrome in traditional Chinese medicine and clinical role of target gene,” Chinese Journal of Integrative Medicine, vol. 15, no. 2, pp. 101–106, 2009.
- W. Jie, X. Yanwei, C. Janxin, and G. Yonghong, “Discovering syndromes in Coronary Heart Disease by cluster algorithm based on random neural network,” in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09), pp. 1–4, June 2009.
- W. Xian, L. Zhong-Xiang, G. Jun-bo, Z. Zhen-Xian, and S. Lin, “Relationship between Traditional Chinese Medicine Syndrome type and coronary arteriography of acute coronary syndrome,” Chinese Journal of Integrative Medicine, vol. 9, pp. 116–119, 2003.
- Z. Y. Gao, J. C. Zhang, H. Xu et al., “Analysis of relationships among syndrome, therapeutic treatment, and Chinese herbal medicine in patients with coronary artery disease based on complex networks,” Journal of Chinese Integrative Medicine, vol. 8, no. 3, pp. 238–243, 2010.
- Z. H. Jia, Y. S. Li, and Y. L. Wu, “Application of entropy-based complex systems partition method in research on quantizing TCM syndrome diagnostic criteria of angina pectoris,” Chinese Journal of Jntegrated Traditional and Western Medicine, vol. 27, no. 9, pp. 804–806, 2007.
- W. Zhong, Z. Boli, S. Chundi, C. Qiguang, and W. Yongyan, “Multivariate analysis of TCM syndrome of stroke,” Chinese Journal of Integrated Traditional And Western Medicine, vol. 23, no. 2, pp. 106–109, 2003.
- M. Shi and C. Zhou, “An approach to syndrome differentiation in traditional chinese medicine based on neural network,” in Proceedings of the 3rd International Conference on Natural Computation (ICNC '07), vol. 01, pp. 376–380, August 2007.
- N. L. Zhang, S. Yuan, T. Chen, and Y. Wang, “Latent tree models and diagnosis in traditional Chinese medicine,” Artificial Intelligence in Medicine, vol. 42, no. 3, pp. 229–245, 2008.
- Y. Feng, Z. Wu, X. Zhou, Z. Zhou, and W. Fan, “Knowledge discovery in traditional Chinese medicine: state of the art and perspectives,” Artificial Intelligence in Medicine, vol. 38, no. 3, pp. 219–236, 2006.
- J. Chen, Y. Xing, G. Xi et al., Comparison of Four Data Mining Models: Bayes, Neural Network, SVM and Decision Trees in Identifying Syndromes in Coronary Heart Disease, Springer, Heidelberg, Germany, 2007.
- Y. Tu, G. Chen, S. Piao, and J. Guo, “Collection system implementation for four TCM diagnostic methods information of hyperlipemia and research on intelligent symptom classification algorithm,” in Advances in Intelligent and Soft Computing, D. Jin, S. Lin, D. Jin, and S. Lin, Eds., pp. 567–572, Springer, Heidelberg, Germany, 2011.
- K. Yao, L. Zhang, J. Wang, and J. Zhang, “Syndromes classification of the active stage of ankylosing spondylitis in traditional Chinese medicine by cluster analysis of symptoms and signs data,” in Communications in Computer and Information Science, L. Qi and L. Qi, Eds., pp. 657–663, Springer, Heidelberg, Germany, 2011.
- P. Cortez, “Data mining with multilayer perceptrons and support vector machines,” in DATA MINING: Foundations and Intelligent Paradigms, Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis, D. Holmes and L. Jain, Eds., pp. 9–25, Springer, 2012.
- C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998.
- C. Chang and C. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–27, 2011.
- J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik, “Feature selection for SVMs,” in Advances in Neural Information Processing Systems, pp. 668–674, MIT Press, 2000.
- I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.
- J. Zhou, D. P. Foster, R. A. Stine, and L. H. Ungar, “Streamwise feature selection,” Journal of Machine Learning Research, vol. 7, pp. 1861–1885, 2006.
- R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.
- Y. W. Chen and C. J. Lin, “Combining SVMs with various feature selection strategies,” Taiwan University, vol. 207, pp. 315–324, 2006.
- D. Koller and M. Sahami, “Toward optimal feature selection,” Tech. Rep., Stanford InfoLab, 1996.
- J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference San Mateo, Morgan Kaufmann, San Francisco, Calif, USA, 1988.
- Z. F. Cui, B. W. Xu, W. F. Zhang, and J. L. Xu, “Approximate Markov blanket feature selection algorithm,” Chinese Journal of Computers, vol. 30, no. 12, pp. 2074–2081, 2007.
- Z. Zhu, Y. S. Ong, and M. Dash, “Markov blanket-embedded genetic algorithm for gene selection,” Pattern Recognition, vol. 40, no. 11, pp. 3236–3248, 2007.
- N. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian network classifiers,” Machine Learning, vol. 29, no. 2-3, pp. 131–163, 1997.
- A. Ouali, A. Ramdane Cherif, and M. O. Krebs, “Data mining based Bayesian networks for best classification,” Computational Statistics and Data Analysis, vol. 51, no. 2, pp. 1278–1292, 2006.
- I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Fransisco, Calif, USA, 2nd edition, 2005.
- R. B. Remco, Bayesian Network Classifiers in Weka, University of Waikato, 2004.
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009.
- E. Frank, M. Hall, G. Holmes et al., “Weka-a machine learning workbench for data mining,” in Data Mining and Knowledge Discovery Handbook, O. Maimon, L. Rokach, O. Maimon, and L. Rokach, Eds., pp. 1269–1277, Springer, 2010.
- T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
- J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Radiology, vol. 143, no. 1, pp. 29–36, 1982.
- T. Fawcett, “ROC graphs: notes and practical considerations for data mining researchers,” Tech. Rep., HP Laboratories, Palo Alto, Calif, USA, 2003.