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

arXiv:2403.19820v1 Announce Type: new
Abstract: 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 …

abstract arxiv cancer capabilities cs.ai cs.lg dataset decision decision trees diagnostics evaluation focus guidelines human loop machine machine learning machine learning models medical pancreatic cancer paper random study trees type xgboost