Association Rule Mining with Apriori Algorithm for Pediatric Foot Disorders

Abstract

An association rule mining has potential to discover important information of disorder in data. The purpose of this study was to show analysis process of the clinical data to acquire significant information effectively between the foot disorder groups and biomechanical parameters related to symptom by the association rule discovery. The first clinical health records of the total 279 pediatric patients diagnosed with the complex foot disorder, including pes planus basically, were used for study. The Apriori algorithm was applied to discover rule of the foot disorder groups. As the results, we were able to discover 8 rules of the complex foot disorder and confirm major information. In next study, another data mining methodology like neural network will be applied with a careful preprocessing for better analysis of the pediatric foot disorder from now on.