摘要： 中医“症-证”分析在中医诊断学和中医证候分析中非常重要。该文以数据挖掘技术为手段对选取的古方进行“症-证”研究，对古方的主治症状进行规范，挖掘“症-证”之间的关系，从而判定方剂的主治证、兼治证。为了挖掘中医“症-证”之间的关系，提出了基于KNN的挖掘算法和基于贝叶斯的挖掘算法。对比实验证明，基于贝叶斯方法正确率达到65.76%，高于KNN的62.50%。

关键词: 数据挖掘, 贝叶斯方法, KNN算法, 传统中医药

Abstract: Analysis of symptom-syndrome relation in TCM (traditional chinese medicine) is one of the most fundamental and important issues for TCM research, which is of great significance for TCM diagnostics and syndrome research. Data mining technology is adopted to analyze the relation on the available ancient prescriptions. Primary symptoms of the ancient prescriptions are standardized. The relation is mined and the mined knowledge is used to judge primary and secondary syndromes of the prescriptions. The mined knowledge reveals the rules about the relation in TCM. Algorithms based on KNN and Bayesian methods are proposed to mine the relation in TCM. And the comparative experiments show that the correct rate of the algorithm based on Bayesian method achieves 65.76%, and is higher than that of the KNN method whose correct rate is 62.50%.

Key words: data mining, Bayesian method, KNN method, traditional Chinese medicine

中图分类号: 

• TP311