基于分组联接的Apriori算法的中医药诊疗关联性分析

Correlation analysis of TCM diagnosis and treatment based on Apriori algorithm with grouping association

目的:高效准确地分析中医药诊疗数据之间的关联性,为科研临床研究提供依据。方法:针对中医药诊疗数据的特点,提出并设计一种改进的Apriori算法——分组联接Apriori算法,用某院中医脑病治疗数据库的数据进行关联性分析实验,以验证该算法的可行性。结果:该算法对联接属性进行了分组操作,减少了大量不相关数据的联接操作,提高了算法的效率。结论:改进后分组联接Apriori算法的执行时间明显低于经典Apriori算法,该算法能够更好地为中医药诊疗数据的科研临床应用服务。

Objective To analyze the correlation between the TCM diagnosis and treatment data to provide support for scientific research and clinical treatment. Methods The characteristics of TCM diagnosis and treatment data were analyzed.An improved Apriori algorithm with grouping association was put forward, and association analysis on the data from the encephalopathy database of some hospital was carried out to verify the feasibility of the algorithm. Results Grouped operation was executed for association properties to reduce the association between noncorrelated data, so that the efficiency of the algorithm was enhanced greatly. Conclusion Improved Apriori algorithm with grouping association consumes shorter time than the classical one, and thus is worthy promoting in TCM diagnosis and treatment data application.