Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks

ABSTRACT Congenital Heart Disease is one of the major causes of deaths in children. However, a proper diagnosis at an early stage can result in significant life saving. Unfortunately, all the physicians are not equally skilled, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. In the present paper, a Decision Support System has been proposed for diagnosis of Congenital Heart Disease. The proposed system is designed and developed by using MATLAB’s GUI feature with the implementation of Backpropagation Neural Network. The Backpropagation Neural Network used in this study is a multi layered Feed Forward Neural Network, which is trained by a supervised Delta Learning Rule. The dataset used in this study are the signs, symptoms and the results of physical evaluation of a patient. The proposed system achieved an accuracy of 90%.