Classification of Heart Disease using Artificial Neural Network and Feature Subset Selection

Abstract

Now a day s artificial neural network ANN has been widely used as a tool for solving many decision modeling problems A multilayer perception is a feed forward ANN model that is used extensively for the solution of a no of different problems An ANN is the simulation of the human brain It is a supervised learning technique used for non linear classification Coronary heart disease is major epidemic in India and Andhra Pradesh is in risk of Coronary Heart Disease Clinical diagnosis is done mostly by doctor s expertise and patients were asked to take no of diagnosis tests But all the tests will not contribute towards effective diagnosis of disease Feature subset selection is a preprocessing step used to reduce dimensionality remove irrelevant data In this paper we introduce a classification approach which uses ANN and feature subset selection for the classification of heart disease PCA is used for preprocessing and to reduce no Of attributes which indirectly reduces the no of diagnosis tests which are needed to be taken by a patient We applied our approach on Andhra Pradesh heart disease data base Our experimental results show that accuracy improved over traditional classification techniques This system is feasible and faster and more accurate for diagnosis of heart disease