Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment
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http://hdl.handle.net/2183/34181
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Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer TreatmentAutor(es)
Fecha
2023Resumen
[Abstract] The increasing adoption of artificial intelligent systems at sensitive domains where
humans are particularly, such as medicine, has provided the context to deeply explore ways of
making machine learning models (ML) understandable for their final users. The success of such
systems require the trust of their users, and thus there is a need to design and provide methods
to understand the decisions made by such systems. We start from a public Pancreatic Cancer
dataset and experiment with different ML models on a diagnosis scenario with the goal to decide
whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis
results we explore different explainability approaches: Decision Tree, Random Forest, and
model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment
set of rules. The increasing adoption of artificial intelligent systems at sensitive domains
where humans are particularly, such as medicine, has provided the context to deeply explore
ways of making machine learning models (ML) understandable for their final users. The success
of such systems require the trust of their users, and thus there is a need to design and provide
methods to understand the decisions made by such systems. We start from a public Pancreatic
Cancer dataset and experiment with different ML models. To validate the diagnostic results
we explore different explainability approaches: Decision Tree based approach, Random Forest
based approach, and different model agnostic ad-hoc approaches, and we compare them against
a standard Pancreatic Cancer treatment set of rules
Palabras clave
Inteligencia artificial
Aprendizaje automático
Medicina-Informática
Cáncer de páncreas
Aprendizaje automático
Medicina-Informática
Cáncer de páncreas
Descripción
Cursos e Congresos, C-155
Versión del editor
Derechos
Attribution 4.0 International (CC BY 4.0)