Heart Disease Diagnosis using Support Vector Machine

Abstract

Medical diagnosis is considered an art regardless of all standardization efforts made, which is greatly due to the fact that medical diagnosis necessitates an expertise in coping with uncertainty simply not found in today's computing machinery. The researchers are encouraged by the advancement in computer technology and machine learning techniques to develop software to assist doctors in making decision without necessitating the direct consultation with the specialists. In this paper, application of artificial intelligence in typical heart disease diagnosis has been investigated. In this research, an intelligent system based support vector machine along with a radial basis function network is presented for the diagnosis. Expert system based on clinical symptoms is used to decide what type of heart disease is possible to appear for a patient, whether it is heart attack or not. The support vector machine with sequential minimal optimization algorithm is applied to India based patients' data set. Then, the Radial Basis Function (RBF) network structure trained by Orthogonal Least Square (OLS) algorithm is applied to same data set for predictions. Results obtained show that support vector machine can be successfully used for diagnosing heart disease. The role of effective diagnosis and the advantages of data training on machine learning- based automatic medical diagnosis system are suggested by the outcomes.