A data visualisation method for investigating the reliability of a high-dimensional low-back-pain MLP network.

This study uses a new data visualization method, developed by the first author, to investigate the reliability of a real world low-back-pain Multi-layer Perceptron (MLP) network from a hidden layer decision region perspective. Using decision region identification information from an explanation facility, the MLP training examples are discovered to occupy decision regions in contiguous class threads across the 48-dimensional input space. MLP testing cases show a similar distribution and consistency within the contiguous threads but with a reduced reliability. Three test regions outside the network’s knowledge bounds are situated between training regions with a consistent classification.