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

TL;DR

This paper introduces REANN, an algorithm that extracts human-readable rules from trained neural networks to improve interpretability in medical diagnosis tasks, maintaining high accuracy.

Contribution

The paper presents a novel rule extraction algorithm from ANNs that automatically determines network structure, prunes irrelevant connections, and discretizes activations for rule generation.

Findings

REANN generates high-quality, interpretable rules.

Rules are comparable in accuracy to other methods.

The approach is effective on benchmark medical datasets.

Abstract

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 phase. Finally, rules are extracted from compact ANNs by examining the discretized activation values of the hidden nodes. Extensive experimental studies on three benchmark classification problems, i.e. breast cancer, diabetes and lenses, demonstrate that REANN can generate high quality rules from ANNs, which are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.