基于数据预处理的贝叶斯网络在中医证候诊断中的应用

目的:探讨中医证候诊断模型的建立,寻找证候诊断标准的可行性方法。方法:针对病例样本少,变量维数高的问题,提出先用层次聚类和主成分分析方法对高维变量进行降维,最后利用生成的主成分进行贝叶斯网络学习和分类。结果:通过数据预处理后,贝叶斯网络分类平均正确率达到了88.75%。结论:基于数据预处理的贝叶斯网络用于中医证候诊断的研究是可行的,为进一步的证候研究提供了借鉴。

Bayesian Network Based on Data Preprocessing Approach to TCM Syndrome Diagnosis

Objective:Discussing the establishment of the model of TCM syndrome diagnosis,providing a feasible method in application of study of TCM syndrome diagnosis criterion.Methods:Considering the problem of few clinical cases and mutildimensional variables,presenting Bayesian network based on data preprocessing in the application of TCM syndrome diagnosis.Combined the methods of hierarchical clustering and principal component analysis to reduce the dimension of mutildimensional variables,finally with the results of principal component analysis,Bayesian network begin to study and classify.Result:Simulation results suggest that TCM syndrome diagnosis model in this paper has high modeling accuracy and reached 88.75% coincidence.Conclusion:It is practical and valid for Bayesian network based on data preprocessing in this paper to be applied to the study of TCM syndrome diagnosis,and will provide much help for the further study.