A DATA MINING APPROACH FOR CLASSIFICATION OF HEART DISEASE DATASET USING NEURAL NETWORK

ABSTRACT Data mining is the process of automating information discovery. ANN is widely used data mining method to extract pattern. Classification is one of the important data mining techniques for classifying given set of input data. In this experiment classification of heart disease dataset is done with the use of Cleveland Heart Disease Dataset. Classification is carried out using neural network classifier MLP .In this experiment performance measures are compared with chosen optimal parameter of MLP neural network, when it is trained and tested over cross validation, the training percentage of 98±0.5 %, testing percentage of 98±1.5% and 97± 1.2% overall accuracy, sensitivity 95±0.5%,specificity 100% are achieved. It shows the consistent performance of MLP neural network as compare to other models. In this work heart disease dataset is classified using 13 input attributes as well as by using 16 inputs attributes. The accuracy difference between 13 attributes and 16 attributes in training dada is 1.67 % and in testing data is 3.7% and in overall accuracy is.1.47%.The results obtained in this experiment shows the efficiency and accuracy of MLP NN.