Bruno Raffael Muchau Santos, Deborah Ribeiro Carvalho


Abstract: In the past few years, many studies have been conducted about KDD, however its incorporation in in health management routines is far from the desired, and much meaningful information has been not used properly. The aim of this article is to present a tool that enables health managers to easily identify association of events that happen in health routines, by post processing the output of the Apriori KDD algorithm. The proposed tool facilitates the discovery of new patters, as it aims to highlight two key points in this process, which are the transitivity between events, and how intense an event association is. The proposed tool was evaluated by potential users, and 100% of them considered the tool helpful in the task of discovering association of events. It is expected that this tool serves as a boost for new researches and studies of KDD and data mining algorithms in the health management area, as it has a growing field of acting.


Knowledge Discovery in Databases; Post-processing, Graph

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