DRNN: Deep Residual Neural Network for Heart Disease Prediction

Abstract

Heart disease is one of the major diseases threatening human health. This paper proposed a novel deep neural network model to predict heart disease based on routine clinical data. We adapt the deep residual structure to discover a novel Deep Residual Neural Network (DRNN). In order to verify the effectiveness of DRNN, we performed experiments on Heart Disease UCI. The accuracy reached 95%, which is better than the traditional machine learning methods among Random Forest 83%, Decision Tree 68%, Logistic Regression 87%, KNN 60%, Native Bayes 80%.