A fuzzy brain emotional learning classifier design and application in medical diagnosis

Abstract

This paper aims to propose a fuzzy brain emotional learning classifier and applies it to medical diagnosis. To improve the generalization and learning ability, this classifier is combined with a fuzzy inference system and a brain emotional learning model. Meanwhile, different from a brain emotional learning controller, a novel definition of the reward signal is developed, which is more suitable for classification. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, the proposed method is applied for the leukemia classification and the diagnosis of heart disease. A comparison between the proposed method with other algorithms shows that this proposed classifier can be viewed as an efficient way to implement medical decision and diagnosis.