Abstract
The data mining analysis of the medication rule and the curative effect of traditional Chinese medicine in treating allergic rhinitis in children was performed by using the association rule Apriori algorithm. The model of interest degree was introduced to improve the Apriori algorithm, and the performance difference of the algorithm before and after improvement was analyzed. Traditional Chinese medicine prescriptions for the treatment of allergic rhinitis in children were selected from the dictionary of Chinese medicine formulations. The frequency, frequent itemsets, and the improved Apriori algorithm of each prescription were analyzed comprehensively. The results showed that both the execution time of the improved Apriori algorithm and the number of mining association rules were signally lower. 102 Chinese herbal compounds were selected, in which the occurrence frequency of Flos magnoliae was the highest (67 times, 5.33%). The occurrence frequency of diaphoretic drugs was the highest (412 times, 32.78%) in drug types. The occurrence frequency of Yu Ping Feng powder was the highest (21 times, 20.59%) in the Chinese herbal compound. After the association rule analysis of the improved Apriori algorithm, Perilla frutescens, Saposhnikovia divaricata, ginseng, Notopterygium root, and Astragalus propinquus Schischkin were often mixed with liquorice, and Flos magnoliae were usually mixed with Fructus xanthii and black plum. Compared with the conditions before treatment, the sign scores of children with allergic rhinitis were remarkably decreased after treatment with traditional Chinese medicine compounds (P < 0.05). The mining performance of the Apriori algorithm was improved by introducing an interest-based model. The treatment of traditional Chinese medicine on allergic rhinitis in children was combined with children's physiological and pathological characteristics of children, which used mild medicines.