Heart disease diagnosis based on deep learning network

Abstract

Heart disease is the leading cause of death, the cardiovascular disease (CVD) is the major cause of the death world wide according to world health organization. Over 30% of global death was because CVD. However it is considered as controllable disease, so early and accurate diagnosis of heart disease is essential to administrating early and optimal treatment in order to increase long –term survival. Early detection can lead to reduce disease progression. In this paper, we propose a new deep neural network that can be used as classifier in heart disease prediction system, the data base is splitted into training and testing parts, the training data are prepressed by extracting its features in order to perform data augmentation, then the augmented data are training by the designed new model that can increase the accuracy of heart disease detection. from the experimental results, the proposed model provide significant improvement in the prediction of the disease in terms of accuracy, sensitivity and specificity as compared with other approaches