Association rule mining in healthcare analytics

Abstract

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. In this work, a novel association rule-mining algorithm is employed for finding various rules for performing valid prediction. Various traditional association mining algorithms has been studied carefully and a new mining algorithm, Treap mining has been introduced which remedies the drawbacks of the current Association Rule Learning (ARL) algorithms. Treap mining is a dynamic weighted priority model algorithm. As it works on dynamic priority, rule creation happens in least time complexity and with high accuracy. When comparing with other association mining algorithms like Apriori and Tertius, we could see that Treap algorithm mines the database in an O(n log n) when compared to Apriori’s O (en) and Tertius’s O (n2). A high precise mining model for the post Liver Transplantation survival prediction was designed using the rules mined by Treap algorithm. United Nations Organ Sharing dataset was used for the study. Rule accuracy of 96.71% was obtained while using Treap mining algorithm where as, Tertius produced 92% and Apriori created 80% valid results. The dataset has been tested in dual environment and significant improvement has been noted for Treap algorithm in both cases.