  Associative classification is a recent and rewarding technique which
integrates association rule mining and classification to a model for prediction
and achieves maximum accuracy. Associative classifiers are especially fit to
applications where maximum accuracy is desired to a model for prediction. There
are many domains such as medical where the maximum accuracy of the model is
desired. Heart disease is a single largest cause of death in developed
countries and one of the main contributors to disease burden in developing
countries. Mortality data from the registrar general of India shows that heart
disease are a major cause of death in India, and in Andhra Pradesh coronary
heart disease cause about 30%of deaths in rural areas. Hence there is a need to
develop a decision support system for predicting heart disease of a patient. In
this paper we propose efficient associative classification algorithm using
genetic approach for heart disease prediction. The main motivation for using
genetic algorithm in the discovery of high level prediction rules is that the
discovered rules are highly comprehensible, having high predictive accuracy and
of high interestingness values. Experimental Results show that most of the
classifier rules help in the best prediction of heart disease which even helps
doctors in their diagnosis decisions.
