P-SVM Applications in TCM Syndrome Classifications
支持向量机算法在中医证候信息分类中的应用

The paper explores possible applications of Prior knowledge Support Vector Machine (P-SVM) based data mining algorithm in an automatic TCM syndrome classification system. In the study, a TCM syndrome database containing some 300,000 medical records is used as a sample set for algorithm training and test. In addition, a range of TCM syndrome theories are incorporated into a prior knowledge set. The sample set is made part of the SVM model, with weighted sequence for classification. The confidence value for each result is also calculated on an individualized basis. It is proved that with prior TCM knowledge, the accuracy of the automatic TCM syndrome classification system can be raised to a level as high as 95%. It is concluded that P-SVM has made a successful application of statistical learning theory (SLT) to the given samples, though limited in number, which heralds an effective approach for improving automatic TCM syndrome classification, and proves the applicab e features of datamining in TCM syndrome researches. The results show that P-SVM marries prior knowledge with the trained samples, and it is an effective a lgorithm for TCM syndrome classification.