Abstract: The analysis of disease-related risk factors is an important part of application of data mining theory in the medical field,which is helpful for doctors to analyze causes of disease and carry out effective work of disease prevention and control.But disease data in the medical field have their own characteristics,such as high imbalance,which means that most valuable information is contained in the attribute items with a small support.It is easy to lose important information when applying the classical association rule algorithm based on the support directly.Therefore,based on the knowledge of medical field and the common statistical standard of medical field——Relative Risk,this paper proposes a mining algorithm for high relative risk itemsets(MARRI) and two corresponding pruning methods,which are interaction pruning and sample number pruning,and verifies the algorithm on the dataset of children’s congenital heart disease.Experimental results show that the algorithm is effective to mine the information in low support items and disease-related factors mined out are more valuable.

Key words: Association rules, Data mining, Disease analysis, Relative risk