  Neural networks (NNs) have been successfully applied to solve a variety of
application problems involving classification and function approximation.
Although backpropagation NNs generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., their predictions cannot be explained as those of decision trees. In many
applications, it is desirable to extract knowledge from trained NNs for the
users to gain a better understanding of how the networks solve the problems. An
algorithm is proposed and implemented to extract symbolic rules for medical
diagnosis problem. Empirical study on three benchmarks classification problems,
such as breast cancer, diabetes, and lenses demonstrates that the proposed
algorithm generates high quality rules from NNs comparable with other methods
in terms of number of rules, average number of conditions for a rule, and
predictive accuracy.
