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

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