Evaluating Explanatory Capabilities of Machine Learning Models in
  Medical Diagnostics: A Human-in-the-Loop Approach
José Bobes-Bascarán Eduardo Mosqueira-Rey Ángel Fernández-Leal Elena Hernández-Pereira David Alonso-Ríos Vicente Moret-Bonillo Israel Figueirido-Arnoso Yolanda Vidal-Ínsua
This paper presents a comprehensive study on the evaluation of explanatory
capabilities of machine learning models, with a focus on Decision Trees, Random
Forest and XGBoost models using a pancreatic cancer dataset. We use
Human-in-the-Loop related techniques and medical guidelines as a source of
domain knowledge to establish the importance of the different features that are
relevant to establish a pancreatic cancer treatment. These features are not
only used as a dimensionality reduction approach for the machine learning
models, but also as way to evaluate the explainability capabilities of the
different models using agnostic and non-agnostic explainability techniques. To
facilitate interpretation of explanatory results, we propose the use of
similarity measures such as the Weighted Jaccard Similarity coefficient. The
goal is to not only select the best performing model but also the one that can
best explain its conclusions and aligns with human domain knowledge.