Classification Research on Syndromes of TCM Based on SVM

2009 2nd International Conference on Biomedical Engineering and Informatics Pub Date : 2009-10-30 DOI:10.1109/BMEI.2009.5305418

Chunming Xia, Feng Deng, Yiqin Wang, Zhaoxia Xu, Guoping Liu, Jin Xu, Helge Gewiss

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引用次数: 7

Abstract

Syndrome is a unique TCM concept, which is an abstractive collection of symptoms and signs. Several modern algorithms have been applied to classify syndromes, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support vector machine (SVM) has been found to be very efficient to solve the classification problems, especially for binary classification with good generalization properties. In this paper, firstly patients’ clinic data of heart disease were preprocessed, then chose the optimal kernel function and used the cross-validation method to find the best parameters for SVM model, finally, the accuracy of testing different syndromes in accordance with pathology of heart disease was obtained. The results indicated that SVM was the best identifier with 81.08% accuracy on samples than the stepwise regression with 77.30% and the neural network with 73.72%. In addition, by comparing with four different kernel functions of SVM, radial basis function (RBF) was the best identifier than the others. Keywords-Syndrome; Traditional Chinese Medicine; Support Vector Machine

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基于支持向量机的中医证候分类研究

证是一个独特的中医概念，是症状和体征的抽象集合。目前已有几种现代算法应用于证候分类，但由于诊断过程复杂，结果并不理想。支持向量机(SVM)是解决分类问题的有效方法，特别是对于具有良好泛化特性的二值分类。本文首先对心脏病患者的临床数据进行预处理，然后选择最优核函数，利用交叉验证的方法寻找SVM模型的最佳参数，最后获得根据心脏病病理检测不同证候的准确率。结果表明，SVM对样本的识别准确率为81.08%，优于逐步回归的77.30%和神经网络的73.72%。此外，通过对比4种不同的支持向量机核函数，径向基函数(RBF)是最优的识别方法。Keywords-Syndrome;中医;支持向量机

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2009 2nd International Conference on Biomedical Engineering and Informatics

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