An Interactive Visualization Tool for Mining, Comparing Association Rules and Frequent Itemsets Across Multiple Datasets

As healthcare data grows in volume and complexity, the use of association rule mining (ARM) and frequent itemset mining (FISM) in disease analysis holds great potential for data-driven decision-making, personalized treatment strategies, and disease prevention. This study introduces an extensible, interactive, self-developed visualization tool designed specifically for ARM and FISM, enabling the intuitive exploration of medical datasets. The tool incorporates an innovative preprocessing method that binarizes datasets from various scaling systems using a systematic multi-threshold evaluation, ensuring standardized analysis across diverse data sources. Its interactive design empowers users to dynamically explore relevant patterns individually, enhancing both the interpretability and usability of customized results. In addition, the tool integrates exploratory statistical assessments to support the interpretation and comparison of resulting association rules (ARs) and frequent itemsets (FISs). In this paper, we evaluate the tool using two pilot datasets: one on symptoms for long COVID and one on incorporating rare diseases (RDs) while also providing sample datasets for user testing.