Discovery of association rules from medical data -classical and evolutionary approaches

Abstract

The paper presents a method of association rules discovering from medical data using the evolutionary approach. The elaborated method (EGAR) uses a genetic algorithm as a tool of knowledge discovering from a set of data, in the form of association rules. The method is compared with known and common method - FPTree. The developed computer program is applied for testing the proposed method and comparing the results with those produced by FPTree. The program is the general and flexible tool for the rules generation task using different data sets and two embodied methods. The presented experiments are performed using the actual medical data from the Wroclaw Clinic.