A NEW NEURAL-NETWORK STRUCTURE FOR DETECTION OF CORONARY HEART-DISEASE

We investigate the application of neural network for the detection of Coronary Heart Disease (CHD). We have 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 Multi-Layered Perceptron (MLP) was trained with risk factors to distinguish CHD. We also describe a modification to the architecture of the neural network in which an extra layer of neurons is added at the input. We 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. We go on to demonstrate the system's capability for detecting both the symptomatic and asymptomatic patient.