Abstract

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be madeaccurately. Therefore, software has been developed based on advanced computer technologies to assist doctors in the diagnostic process.  This work intends to use the Differential Evolution Fuzzy Neural Network (DEFNN) for heart disease diagnosis. The proposed DE is applied to enhance performance of the DEFNN. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. The proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights. The hybrid DEFNN algorithm with DE is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed DEFNN algorithm is estimated using classification accuracy, and the execution time.

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