Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach

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Abstract
This paper presents a broad study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest, and XGBoost using a pancreatic cancer data set. 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 select a pancreatic cancer treatment. These features are not only used as a dimensionality reduction approach for the machine learning models but also as a way to evaluate the explainability capabilities of the different models using agnostic and non-agnostic explainability techniques. To facilitate the interpretation of explanatory results, we propose the use of similarity measures such as the Weighted Jaccard Similarity coefficient. The goal is to select not only the best performing model but also the one that can best explain its conclusions and better aligns with human domain knowledge.
Funding Information
  • Agencia Estatal de Investigación (PID2019-107194GB-I00/AEI/10.13039/501100011033)
  • Agencia Estatal de Investigación (PID2023-147422OB-I00)
  • Xunta de Galicia (ED431C 2022/44)
  • EU European Regional Development Fund
  • Centro de Investigación de Galicia CITIC
  • Xunta de Galicia (ED431G 2023/01)

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