Discovering medical association rules from medical datasets

Abstract

In this paper, we aim to discover interesting medical association rules from medical datasets for prediction purposes. This medical dataset is a data set of patients' records, where each record is a combination of both textual information (personal and medical) and extracted image features for the given patient. We provide an association rule mining system that discovers constrained association rules in medical records that includes numeric, categorical and image features.