Artificial neural networks for the diagnosis of coronary artery disease

Artificial Neural Networks (ANNs) have been widely advocated as tools for solving many decision modeling problems. In this paper, we use ANNs for the prediction of coronary artery disease. Real data from four major international medical organizations are used in the training and testing of the ANN algorithm. To speed up the training time, we implemented the algorithm in parallel on an Intel Paragon parallel computer. We have achieved an accuracy of > 76%, a comparable performance to probabilistic and statistical techniques. Furthermore, with parallel implementation, we achieve the accuracy in < 5 minutes of training time. Compared with the statistical approach, such savings in time are substantial. We conclude, therefore, that ANN is a fast alternative to classical statistical techniques for prediction and modeling of experimental data. Two popular weight-adaptation algorithms, RPROP and Delta-Bar-Delta rules are compared. The effect of network architecture and how to treat missing values for these two algorithms are also investigated. In general, RPROP is more robust and less affected by choice of architecture, order of data presentation, and effect of missing values.