Pathogenic Factors Mining Based on Medical Big Data Using Association Analysis Algorithm: A Case of Anorectal Diseases

Guangdong Province is a district with a high incidence of anorectal diseases. In addition, the pathogenic factors of anorectal diseases are complex and diverse. Existing clinical diagnostic techniques are difficult to accurately analyze the pathogenic factors of anorectal diseases. Therefore, in this paper, an association analysis algorithm is proposed for solving the problem of pathogenic factors mining. In order to dig out the valuable information of medical big data, an improved Apriori algorithm is designed. Compared with the classical Apriori algorithm, by deleting the unrelated itemsets which do not contain the specific items, the proposed Apriori algorithm can decrease the run time. Finally, simulation results show the superiority of the proposed algorithm.