Intelligent assistance for coronary heart disease diagnosis: A comparison study

Abstract

Using only non invasive medical information, we propose inductive decision trees exploiting C4.5 algorithm, artificial neural networks with three MLP models, and linear discriminant analysis to diagnose coronary heart disease. The first neural network model is a constructive MLP called OIL (Orthogonal Incrementing Learning) that builds its hidden neurons during the training phase. The second one is a fixed MLP architecture with the same number of hidden neurons obtained from the first network building methodology. The last one is a special ”interpretable” MLP model with a fixed architecture (IMLP), which is interpretable through symbolic rule extraction. In general, explanation of connectionist model responses are difficult to obtain, especially when input examples have continuous variables. This is not acceptable for real world diagnosis applications. The novelty in our study consists in the interpretability of the IMLP model we have developed. For this diagnosis application, all neural networks globally obtain better predictive accuracies than C4.5 and the linear discriminant analysis. Results obtained with the OIL method are slightly better than those obtained by IMLP, but they lack interpretability.