Mining Rare Association Rules on Banpheo Hospital (Public Organization) via Apriori MSG-P Algorithm

Abstract

Mining association rule is one of the important techniques in data mining to exploit hidden knowledge in large database. Many businesses in several areas need this technique for examine their enormous information, and public health is the one area that highly requires. Several hidden information conceal in daily operation data such as relation between visit time and symptom, relation between disease and patient age, etc. By the way, association rule discovery via traditional Apriori algorithm, the fundamental way to retrieve hidden rules, has to pay with tremendous resources and time. This research implements the modification of association rule mining technique, Apriori MSG-P, in operational database of Banpheo hospital (Public Organization), Sumutsakon province, Thailand. The objectives target on epidemic information and patient behavior on hospital’s services. The research’s outcomes show that our implementation can evaluate a lot of valuable information that can be used by both of operation staffs and executive staffs. Moreover, the research’s outcomes demonstrate that Apriori MSG-P can be the proper one technique that can implement the realworld databases.