MH-ARM: A Multi-Mode and High-Value Association Rule Mining Technique for Healthcare Data Analysis

The association rules mining process enables the end users to analyze, understand, and use the extracted knowledge in an intelligent system or to support the decision-making processes. To find valuable association rules from a large number of redundant rules, this paper proposes a deeper mining process, multi-mode and high value association rules mining (MH-ARM). This method takes into account the category information, the size of the item set, natural semantics, various metrics, and effective visualization of results. The process can effectively reduce the number of rules and improve the value and accuracy of the rules screened out for auxiliary diagnosis. In the end, the experimental data of rhinitis were analyzed and the effectiveness of the process was verified.