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

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 knowledg...

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Autori principali:	José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, David Alonso-Ríos, Israel Figueirido-Arnoso, Yolanda Vidal-Ínsua
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2026-01-01
Serie:	Mathematics
Soggetti:	explainability XAI machine learning jaccard similarity pancreatic cancer
Accesso online:	https://www.mdpi.com/2227-7390/14/3/497
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