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Predicting of the heart disease is one of the important issues and many researchers develop intelligent medical systems to enhance ability of the physicians. In this paper we offer an intelligent system that diagnose and classify the severity of the disease due to heart failure. This system will use attribute filtering techniques genetic algorithm that has been known to be a very adaptive and efficient method of feature selection and reduce number of attributes which indirectly reduces the number of diagnosis tests which are needed to be taken by a patient. The classification techniques such as Support Vector Machines, Naive Bayesian Theorem, nearest neighbor and Linear discriminant analysis are used in this paper to know the classification accuracy of the techniques in the prediction of the heart disease. Apply proposed system on the Cleveland Heart Disease database. Then compare the results with other techniques according to using the same data.

Linear Discriminant Analysis for An Efficient Diagnosis of Heart Disease via Attribute Filtering Based on Genetic Algorithm