Classification of Heart Disease Using Naïve Bayes and Genetic Algorithm

Abstract

Data mining techniques have been widely used to mine knowledgeable information from medical data bases. In data mining Classification is a supervised learning that can be used to design models describing important data classes, where class attribute is involved in the construction of the classifier. Naive Bayes is very simple, most popular, highly efficient and effective algorithm for pattern recognition. Medical data bases are high volume in nature. If the data set contains redundant and irrelevant attributes, classification may produce less accurate result. Heart disease is the leading cause of death in India as well as different parts of world. Hence there is a need to define a decision support system that helps clinicians to take precautionary measures. In this paper we propose a new algorithm which combines Naive Bayes with genetic algorithm for effective classification. Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.