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Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment
dc.contributor.author | Bobes-Bascarán, José | |
dc.contributor.author | Fernández-Leal, Ángel | |
dc.contributor.author | Mosqueira-Rey, E. | |
dc.contributor.author | Alonso Ríos, David | |
dc.contributor.author | Hernández-Pereira, Elena | |
dc.contributor.author | Moret-Bonillo, Vicente | |
dc.date.accessioned | 2023-11-13T16:44:21Z | |
dc.date.available | 2023-11-13T16:44:21Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/2183/34181 | |
dc.description | Cursos e Congresos, C-155 | es_ES |
dc.description.abstract | [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 | es_ES |
dc.description.sponsorship | Xunta de Galcia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galcia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | This work has been supported by the State Research Agency of the Spanish Government (grant PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia (grant ED431C2022/44), supported in turn by the EU European Regional Development Fund. We wish to acknowledge support received from the Centro de Investigaci ´on de Galicia CITIC, funded by the Xunta de Galicia and the European Regional Development Fund (Galicia 2014-2020 Program; grant ED431G 2019/01) | |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107194GB-I00/ES/ANÁLISIS DE ESTRATEGIAS PARA INCORPORAR HUMANOS AL PROCESO DE APRENDIZAJE AUTOMÁTICO Y SU APLICACIÓN A LA INVESTIGACIÓN DEL CÁNCER PANCREÁTICO | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.000024.28 | |
dc.rights | Attribution 4.0 International (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/deed.es | * |
dc.subject | Inteligencia artificial | es_ES |
dc.subject | Aprendizaje automático | es_ES |
dc.subject | Medicina-Informática | es_ES |
dc.subject | Cáncer de páncreas | es_ES |
dc.title | Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 175 | es_ES |
UDC.endPage | 182 | es_ES |
UDC.conferenceTitle | VI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruña | es_ES |