Propose a Enhanced Framework for Prediction of Heart Disease

Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test\ud etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way\ud of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health\ud care data is contained by the health care industry. The effective decisions are made by means of this hidden\ud information. For appropriate results, the advanced data mining techniques with the information which is based\ud on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical\ud techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural\ud networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental\ud phenomena and these can be understand by using the theory of ANN. The problem of probability and induction\ud can be arised for the inference and classification because these are the powerful instruments of ANN. In this\ud paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are\ud used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle\ud Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection\ud in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the\ud above techniques, the framework will take the input dataset and fed into the feature selection techniques block,\ud which selects any one techniques that gives the least number of attributes and then classification task is done\ud using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of\ud heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the\ud user to know the important attributes based on the proposed framework