A neural network approach to the detection of coronary artery disease

Abstract

The authors used a neural network (NN) on data from a self-applied questionnaire to implement a decision system designed to seek out high risk individuals in a large population. A multilayered perceptron was trained with various risk factors to distinguish coronary heart disease. The performance of the NN was evaluated by receiver operating characteristic (ROC) analysis. A maximum ROC area of 98% was obtained. The authors also describe a modification to the architecture of the neural network in which an extra layer of neurons is added at the input. They present possible interpretations of the weights of these neurons and show how they can be used as a selection criteria for which questions to use as inputs. The technique is compared against other statistical methods. The authors go on to demonstrate the system's capability for detecting both the symptomatic and asymptomatic patient.<>