  Medical imaging diagnosis increasingly relies on Machine Learning (ML)
models. This is a task that is often hampered by severely imbalanced datasets,
where positive cases can be quite rare. Their use is further compromised by
their limited interpretability, which is becoming increasingly important. While
post-hoc interpretability techniques such as SHAP and LIME have been used with
some success on so-called black box models, the use of inherently
understandable models makes such endeavors more fruitful. This paper addresses
these issues by demonstrating how a relatively new synthetic data generation
technique, STEM, can be used to produce data to train models produced by
Grammatical Evolution (GE) that are inherently understandable. STEM is a
recently introduced combination of the Synthetic Minority Oversampling
Technique (SMOTE), Edited Nearest Neighbour (ENN), and Mixup; it has previously
been successfully used to tackle both between class and within class imbalance
issues. We test our technique on the Digital Database for Screening Mammography
(DDSM) and the Wisconsin Breast Cancer (WBC) datasets and compare Area Under
the Curve (AUC) results with an ensemble of the top three performing
classifiers from a set of eight standard ML classifiers with varying degrees of
interpretability. We demonstrate that the GE-derived models present the best
AUC while still maintaining interpretable solutions.
