Objective:
Artificial Neural Networks (ANNs) trained with backpropagation learning algorithm have been used commonly in previous studies. This study presents radial basis function neural network (RBFNN), a special kind of neural network, and logistic regression analysis (LRA) for prog-nostic classification of Coronary Artery Disease (CAD).
Methods:
The records of 237 consecutive people who had been referred for the department of Cardiology were used in the analysis. Radial basis function neural network and logistic regression analysis were used for CAD classification.
Results:
The results have shown that LRA and RBFNN were both successful for classification and might be used for non-invasively based on clinical variables in the classification of diseases like CAD.
Conclusions:
The work can be concluded that LRA performed the classification better than RBFNN for prognostic CAD classification in the present CAD data. However, RBFNN, utilizing larger sample sizes, can have better classification accuracy. For more definite comparison, simulation studies should be carried out using various methods.
Keywords: Coronary artery disease,Classification,Logistic regression analysis,Radial basis function neural network.
