An Intelligent System for Heart Disease Diagnosis using Regularized Deep Neural Network

Abstract

Heart disease is one of the deadly diseases. Timely detection of the disease can prevent mortality. In this paper, an intelligent system is proposed for the diagnosis of heart disease using clinical parameters at early stages. The system is developed using the Regularized Deep Neural Network model (Reg-DNN). Cleveland heart disease dataset has been used for training the model. Regularization has been achieved by using dropout and L2 regularization. Efficiency of Reg-DNN was evaluated by using hold-out validation method.70% data was used for training the model and 30% data was used for testing the model. Results indicate that Reg-DNN provided better performance than conventional DNN. Regularization has helped to overcome overfitting. Reg-DNN has achieved an accuracy of 94.79%. Results achieved are quite promising as compared to existing systems in the literature.  Authors developed a system containing a graphical user interface. So, the system can be easily used by anyone to diagnose heart disease using the clinical parameters.