Extracting Symbolic Rules for Medical Diagnosis Problem

TL;DR

This paper presents an algorithm to extract symbolic rules from neural networks trained on medical diagnosis data, making their decision processes more interpretable while maintaining high accuracy.

Contribution

The paper introduces a novel algorithm for extracting high-quality symbolic rules from neural networks applied to medical diagnosis problems.

Findings

Rules are comparable to other methods in quality and accuracy.

The algorithm performs well on breast cancer, diabetes, and lenses datasets.

Extracted rules enhance interpretability of neural network predictions.

Abstract

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.