An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems

Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, called rule extraction from ANNs (REANN), to extract rules from trained ANNs for medical diagnosis problems. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. In the first phase, the number of hidden nodes in ANNs is determined automatically by a constructive algorithm. In the second phase, irrelevant connections and input nodes are removed from trained ANNs without sacrificing the predictive accuracy of ANNs. The continuous activation values of the hidden nodes are discretized by using an efficient heuristic clustering algorithm in the third...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: S. M. Kamruzzaman, Md. Monirul Islam
• Keywords: Classification Problems, Compact Anns, CORR 2010, Education, Hidden Nodes