Main Article Content
To predict and diagnose heart disease various methods based on machine learning were presented. Before occurrence of heart attack, to treat cardiac patients, it is significant to accurate heart disease prediction. Existing methods failed to improve performance of heart disease prediction and use conventional method to choose features from dataset. In this paper, proposed for heart disease prediction feature extraction approaches and classification using ensemble deep learning. First, Feature extraction using SIFT and ALEXANET from the Mask Region-Based Convolutional Neural Network (RCNN) instance segmented image. Second one, Hybrid Classification with the combination of Random forest and Gaussian Navies Bayes to detect the heart attack. Proposed method is calculated with heart disease data and then testing and training data is compared achieves better results. This outcome indicates that our method is more effective for heart attack prediction.