A support system for coronary artery disease detection using a deep dense neural network

Because of the advancement of present-day contraptions and hardware, human life has turned out to be very extravagant. Consequently physical endeavours for performing any work are lessening step by step. This leads an individual more inclined to coronary artery sickness which is an assortment of heart disorders. It has become the chief reason for mortality in the entire world. For a better and accurate identification of the disease, different researchers have explored many intelligent prediction systems. In this paper, an effective coronary artery disease prediction system is proposed using a deep dense neural network. The proposed model is an adaptive version of a dense neural network with the addition of deep hidden layer structure and dropout. Here, the data is collected from heart disease data sets present in the UCI repository. The classifier has shown a classification accuracy of 95.32%.