Prediction of risk score for heart disease using associative classification and hybrid feature subset selection

Medical data mining is the search for relationships and patterns within the medical data that could provide useful knowledge for effective medical diagnosis. Extracting useful information from these data bases can lead to discovery of rules for later diagnosis tools. Generally medical data bases are highly voluminous in nature. If a training data set contains irrelevant and redundant features classification may produce less accurate results. Feature selection as a pre-processing step in used to reduce dimensionality, removing irrelevant data and increasing accuracy and improves comprehensibility. Associative classification is a recent and rewarding technique that applies the methodology of association into classification and achieves high classification accuracy. Most associative classification algorithms adopt exhaustive search algorithms like in Apriori, and generate huge no. of rules from which a set of high quality of rules are chosen to construct efficient classifier. Hence generating a small set of high quality rules to build classifier is a challenging task. Cardiovascular diseases are the leading cause of death globally and in India more deaths are due to CHD. Cardiovascular disease is an increasingly an important cause of death in Andhra Pradesh. Hence there is an urgent need to develop a system to predict the heart disease of people. This paper discusses prediction of risk score for heart disease in Andhra Pradesh. We generated class association rules using feature subset selection. These generated rules will help physicians to predict the heart disease of a patient.