ENHANCED PREDICTION OF HEART DISEASE WITH FEATURE SUBSET SELECTION USING GENETIC ALGORITHM

Clinical diagnosis is done mostly by doctor’s expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. The objective of our work is to predict more accurately the presence of heart disease with reduced number of attributes. Originally, thirteen attributes were involved in predicting the heart disease. In our work, Genetic algorithm is used to determine the attributes which contribute more towards the diagnosis of heart ailments which indirectly reduces the number of tests which are needed to be taken by a patient. Thirteen attributes are reduced to 6 attributes using genetic search. Subsequently, three classifiers like Naive Bayes, Classification by clustering and Decision Tree are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. Also, the observations exhibit that the Decision Tree data mining technique outperforms other two data mining techniques after incorporating feature subset selection with relatively high model construction time. Naive Bayes performs consistently before and after reduction of attributes with the same model construction time. Classification via clustering performs poor compared to other two methods.