A Genetic-Algorithm-Based Method for Optimization of Fuzzy Reasoning and Its Application to Classification of Heart Disease from Ultrasonic Images

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  • Genetic-Algorithm-Based Method for Optimization of Fuzzy Reasoning and Its Application to Classification of Heart Disease from Ultrasonic Images

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Abstract

This paper describes a method for optimizing the parameters of fuzzy rules using genetic algorithms (GAs) for classification of myocardial heart disease from ultrasonic images. Gaussian-distributed membership functions (GDMFs) constructed from the texture features inherent in the ultrasound images are used, and the coefficients acted as a set of parameters to adjust the magnitudes of the standard deviations of the GDMFs are employed. Optimal coefficients are determined through training process using the GA. The GA-based fuzzy classifier is used to discriminate two sets of echocardiographic images, namely, normal case (23 samples) and abnormal case (22 samples), diagnosed by a highly trained physician. The results of our experiments are very promising. In the best case, we achieve a classification rate of 95.8%. The results indicate that the method has potential utility for computer-aided diagnosis of myocardial heart disease.

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