Fast rule-based heart disease prediction using associative classification mining

Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Healthcare industry collects large amounts of data which are not mined to discover hidden information for taking decision. Increasing access to huge datasets and corresponding demands to analyze these data has led to the development of new online algorithms for performing machine learning on data streams. There is a need to develop a decision support system for predicting the huge datasets generated by healthcare industry. In this paper we proposed an efficient technique for heart disease prediction. This research uses associative classification which builds a classifier with prediction rules of high interestingness values. Experimental results show that this work helps doctors in their diagnosis decisions.