Rule extraction from neural networks for medical domains

Neural networks are a powerful classification technique that are capable of discovering complex relationships between inputs and outputs. This powerful classification ability has meant that neural networks have been widely applied to medical domains. However, neural networks suffer from the socalled blackbox problem: they predict accurately but offer no explanation of how the decision has been derived. This lack of explanation has limited their adoption and has been a concern to the medical community. This paper demonstrates ExTree a rule extraction algorithm being applied to neural networks which have been trained on medical datasets. ExTree extracts from the neural network a decision tree which is a graphical and easily understood representation of a decision process.

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