Classification Research on Syndromes of TCM Based on SVM

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.