A fuzzy ART2 model for finding association rules in medical data

This paper describes a model that discovers association rules from a medical database to help doctors treat and diagnose a group of patients who show similar prehistoric medical symptoms. The proposed data mining procedure consists of two modules. The first is a clustering module that is based on a neural network, Adaptive Resonance Theory 2 (ART2), which performs affinity grouping tasks on a large amount of medical records. The other module employs fuzzy set theory to extract fuzzy association rules for each homogeneous cluster of data records. In addition, an example is given to illustrate this model. Simulation results show that the proposed algorithm can be used to obtain the desired results with a reduced processing time.