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Intelligent assistance for coronary heart disease diagnosis: A comparison study

  • Guido Bologna
  • Ahmed Rida
  • Christian Pellegrini
Diagnostic Problem Solving
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1211)

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.

Keywords

Hide Layer Linear Discriminant Analysis Hide Neuron Input Neuron Rule Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Guido Bologna
    • 1
  • Ahmed Rida
    • 1
  • Christian Pellegrini
    • 1
  1. 1.Artificial intelligence group, Computing Science CenterUniversity of GenevaGeneva 4Switzerland

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