Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification

Using a new method published by the first author, this chapter shows how knowledge in the form of a ranked data relationship and an induced rule can be directly extracted from each training case for a Multilayer Perceptron (MLP) network with binary inputs. The knowledge extracted from all training cases can be used to validate the MLP network and the ranked data relationship for any input case provides direct user explanations. The method is demonstrated for example training cases from a real-world MLP that classifies low back pain patients into three diagnostic classes. In using the method to validate the network a number of test cases apparently mis-classified by the network were found to have most likely been incorrectly classified by the clinicians. The method uses a direct approach which does not depend on combinatorial search and is thus applicable to realworld networks with large numbers of input features, as demonstrated in this current study.