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Risk Estimator using a Multi-Layer Perceptron Network for Coronary Artery Disease Prevention

One of the most prevalent heart disease is coronary artery disease (CAD). We propose the use of Deep Learning (DL) Network Multi-Layer Perceptron (MLP) in order to obtain an early cardiovascular risk estimation at 10 year for CAD prevention in patients with the purpose of reduced rate of mistreatment.For this purpose, we designed a protocol for selecting eloquent data. We also designed a method which is using Deep Neural Network sequential model which has multiple inputs and three outputs. Data set are from a private clinic in SouthWest zone in Romania. Custom data set included a batch of 784 patients with 11 medical characteristics. The result of predicting the MLP network gives us the probability that the patient will develop a severe heart disease in the following 10 years. By deploying a DL network, we were able to provide an unitary risk assessment method of CAD for physicians that allowed the “localization” of the medical European Society of Cardiology guidelines to Romania region.