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Automated synthesis of prediction models for neural network based myocardial infarction classifiers

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4 Author(s)
Lopez, J.A. ; Fac. of Informatics, Ulster Univ., Jordanstown, UK ; Nugent, C. ; Black, N D ; Smith, A.E.

Parameter and architectural selection for multiple layered perceptron (MLP) classifiers involve a number of heuristic design procedures. The aim in the design process of such classifiers is to achieve maximum generalization and avoid over-fitting of the training data. It has been the objective of this study to develop a symbolic prediction model to calculate the point at which training should cease for a given neural network (NN) based 12-lead ECG classifier to ensure maximum generalization. This prediction model has been obtained by means of genetic programming (GP), where a GP individual has been evolved to generate a symbolic model that predicts the optimal number of training epochs for three different ECG myocardial infarction classifiers: Anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), and combined myocardial infarction (CMI). The GP model demonstrated to be a very accurate method showing no significant differences between the optimal number of epoch values and the predicted values for both train and test data sets for the three aforementioned pathologies.

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Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE  (Volume:4 )

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