aspg-3-1237-1791943056 20261014015736 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2022 8 2 Abedallah Z. Abualkishik American University in the Emirates, Dubai, UAE Rasha Almajed American University in the Emirates, Dubai, UAE Saleh A. Almutairi American University in the Emirates, Dubai, UAE ;Computer and Information Science Department, Taibah University, KSA. Deaths from cardiovascular disease (CVD) are more common than any other kind of mortality in the world. Electrocardiograms, two-dimensional echocardiograms, and stress tests are only a few of the diagnostic tools available to combat the rising incidence of cardiovascular disease. Since the electrocardiogram (ECG) is a clinical therapeutic agent that does not need any intrusive procedures, it may be used to diagnose cardiovascular disease (CVD) early and prescribe the appropriate treatment to prevent its fatal consequences. However, it may be time-consuming and demanding for a physical examination to interpret all these signals from various pieces of equipment, especially if they are non-stationary and repeating. It is necessary to use a computer-assisted model for rapid and precise prediction of CVDs since the Heart Signal from an ECG machine is not a stationary sign, the differences may not be repeated and may manifest at different intervals. In this paper, we offer a fully deep convolutional neural network-based automated diagnosis technique for cardiovascular illness. In order to extract those form characteristics from the Kaggle cardio-vascular disease dataset, CVD-MRI is employed in this detection method. In this case, the risk of cardiovascular disease is estimated using a completely deep convolution neural network and deep learning convolution filters (CVD). The suggested operation's main goal is to "improve the accuracy of completely deep convolution neural network while simultaneously reducing the complexity of the computation and the cost function." Accuracy of 88 percent is achieved by the proposed fully deep convolutional neural network. 2022 16 24 1237 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.080202 https://americaspg.com/journal/3/article/1237 https://americaspg.com/storage/3493.pdf