Digging deep into weighted patient data through multiple-level patterns

Abstract

Large data volumes have been collected by healthcare organizations at an unprecedented rate. Today both physicians and healthcare system managers are very interested in extracting value from such data. Nevertheless, the increasing data complexity and heterogeneity prompts the need for new efficient and effective data mining approaches to analyzing large patient datasets. Generalized association rule mining algorithms can be exploited to automatically extract hidden multiple-level associations among patient data items (e.g.,

A novel pruning algorithm for mining long and maximum length frequent itemsets

“…Association rule mining [1] is one of the most popular exploratory data mining techniques for extrapolating interesting and previously unknown patterns from large volumes of data. Despite having originally been developed for market basket analysis purposes [2], recently there has been growing attention towards using these association rules in many other applications, such as in medical datasets [3]. These association rules are used principally to find the most frequent values of a set of variables (discovery of frequent itemsets), and then detect the relationships between the frequent items (rule generation) [4].…”