An enhanced generalized adaptive resonance theory neural network and its application to medical pattern classification

Generalized Adaptive Resonance Theory (GART) is a neural network model based on the integration of Gaussian ARTMAP and the Generalized Regression Neural Network. As demonstrated in our previous work, GART is capable of online learning and is effective in tackling both classification and regression tasks. In this paper, we propose an Enhanced GART (EGART) network whereby the capability of GART is further enhanced with the Laplacian function, a new vigilance function, a new match-tracking mechanism, and a fuzzy rule extraction procedure. The applicability of EGART to pattern classification and fuzzy rule extraction problems is evaluated using three benchmark medical data sets and one real medical diagnosis problem. The experimental results are analyzed, discussed, and compared with other reported results. The outcomes demonstrate that EGART is capable of producing high accuracy rates and of extracting useful rules for tackling medical pattern classification problems.