Abstract: Objective: To use the statistical association rule apriori algorithm to explore the distribution and combination laws of the disease location,disease nature and syndrome types of chronic cough in children.Methods: The literatures on children′s chronic cough in the literature databases were searched,common syndrome factors were extracted,the frequency distribution of disease location and disease nature was obtained,and the names of common syndrome factors were got through cluster analysis.The common syndrome elements and their corresponding syndrome types were counted.Through the apriori algorithm the association rules among the various syndrome elements were worked out to reflect the etiology and pathogenesis of chronic cough in children.Results: According to the statistics of the classification and combination of syndrome types,disease locations,and disease factors in the literature,a total of 145 qualified literatures on chronic cough in children were collected,and a total of 1 043 syndrome factors were recorded.Among them,the disease location syndrome factors were lung,spleen,kidney,stomach,and liver had the highest frequency.Disease syndrome elements such as phlegm,fire,qi inverse,wind,dryness and dampness had the highest frequency.Association rules:(1)Exogenous chronic cough:the location of the disease was related to the disease element:wind,dryness ==> lung (0.286).(2)Internal traumatic chronic cough:the location of the disease element was correlated:spleen ==> lung(0.808).Disease syndrome factors were correlated:wet ==> heat(0.649);disease nature was related to disease location syndrome factors:Qi deficiency ==> lung,spleen(0.863).Conclusion: Based on the physiological characteristics of children with three deficiencies and two excesses,the main diseases locations of children′s chronic cough are lung,spleen,kidney and liver;it is closely related to water metabolism,which provides a dialectical idea for the clinical differentiation of chronic cough from the perspective of dryness and dampness.

Key words: Chronic cough, Syndromes, Data mining, Cluster analysis, Association rules, Apriori algorithm, Children