Predictive Hybrid Approach Method to Detect Heart Disease

Data Mining has indicated a capable result in prediction and detection of Heart illness, the data removal methods are widely applied which are used for predictions, identification in different type of heart diseases. The discipline of this process is to extract the data and information from the large dataset by combining methods of artificial intelligence and database management. Here a Proposed method is used to predict the better approach followed by others. According to the comparative learning of different data removal method are applied which are used as predictions of heart diseases and similar medical problems. Diverse types of algorithms which are tied with data removal which has helped in educating the performance in medical domain this paper comprises of  proposed approach of hybrid method to determine the Heart Syndrome based on artificial neural network. As the first approach followed by normal ANN (artificial neural network) which consists of vast arrangement of layers and is applied on the dataset. This gives a view to use neural networks.  Here the accuracy is calculated by applying hidden layer of auto encoder surrounded by the layers of neural network which is high comparable with others .Considering and comparing both the cases without auto encoder and with auto encoder is to be  calculated. The performance is more robust as compared with the other methods and literature papers. The results calculated in this hybrid approach gives better and optimized solution.