  In the pursuit of enhancing the efficacy and flexibility of interpretable,
data-driven classification models, this work introduces a novel incorporation
of user-defined preferences with Abstract Argumentation and Case-Based
Reasoning (CBR). Specifically, we introduce Preference-Based Abstract
Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users
to define multiple approaches to compare cases with an ordering that specifies
their preference over these comparison approaches. We prove that the model
inherently follows these preferences when making predictions and show that
previous abstract argumentation for case-based reasoning approaches are
insufficient at expressing preferences over constituents of an argument. We
then demonstrate how this can be applied to a real-world medical dataset
sourced from a clinical trial evaluating differing assessment methods of
patients with a primary brain tumour. We show empirically that our approach
outperforms other interpretable machine learning models on this dataset.
