An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Abstract

Evolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the diagnosis and treatment of diseases from this vast data has become imperative. Association rule mining is an obvious choice for representing this previously hidden information as rules are simple to understand and infer. These rules can be used to understand the etiology of diseases and classify patients based on recorded characteristics. The interestingness of such an algorithm for rule mining will be determined by its accuracy and ability to produce easily understandable rules. This study applies latest improvements in swarm intelligence to devise a novel strategy for rule mining that exhibits high predictive accuracy and comprehensibility. It has been applied over four medical datasets to classify patients as fit or unfit. The paper begins with an explanation of rule mining functionality and concept of swarm intelligence. The current techniques for rule mining in the medical domain are surveyed and their shortcomings are identified. This is followed by a description of the proposed algorithm which includes a novel rule discovery procedure and a novel rule list selection criterion. The results of the proposed algorithm thus obtained, are compared with the other best known approaches. Finally, the future scope of work in this area is briefly discussed.