Argumentation Theory for Decision Support in Health-Care: A Comparison with Machine Learning

Abstract

This study investigates role of defeasible reasoning and argumentation theory for decision-support in the health-care sector. The main objective is to support clinicians with a tool for taking plausible and rational medical decisions that can be better justified and explained. The basic principles of argumentation theory are described and demonstrated in a well known health scenario: the breast cancer recurrence problem. It is shown how to translate clinical evidence in the form of arguments, how to define defeat relations among them and how to create a formal argumentation framework. Acceptability semantics are then applied over this framework to compute arguments justification status. It is demonstrated how this process can enhance clinician decision-making. A well-known dataset has been used to evaluate our argument-based approach. An encouraging 74% predictive accuracy is compared against the accuracy of well-established machine-learning classifiers that performed equally or worse than our argument-based approach. This result is extremely promising because not only demonstrates how a knowledge-base paradigm can perform as well as state-of-the-art learning-based paradigms, but also because it appears to have a better explanatory capacity and a higher degree of intuitiveness that might be appealing to clinicians.