On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis

Nonalcoholic steatohepatitis (NASH) is a common disease that ultimately can lead to the development of end-stage liver disease, cirrhosis, or hepatocellular carcinoma. An early prediction of NASH provides an opportunity to make an appropriate strategy for prevention, early diagnosis, and treatment....

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Autores: Babakov, Nikolay, Rezgova, Elena, Reiter, Ehud, Bugarín-Diz, Alberto
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Santiago de Compostela (USC)
Repositorio:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:inglés
OAI Identifier:oai:minerva.usc.gal:10347/43406
Acceso en línea:https://hdl.handle.net/10347/43406
Access Level:acceso abierto
Palabra clave:Textual explanations
Natural Language Generation
Explainable Machine Learning
Non-Alcoholic Fatty Liver Disease (NAFLD)
NonAlcoholic SteatoHepatitis (NASH)
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spelling On the role of explanations in machine learning prediction of nonalcoholic steatohepatitisBabakov, NikolayRezgova, ElenaReiter, EhudBugarín-Diz, AlbertoTextual explanationsNatural Language GenerationTextual explanationsExplainable Machine LearningNon-Alcoholic Fatty Liver Disease (NAFLD)NonAlcoholic SteatoHepatitis (NASH)Nonalcoholic steatohepatitis (NASH) is a common disease that ultimately can lead to the development of end-stage liver disease, cirrhosis, or hepatocellular carcinoma. An early prediction of NASH provides an opportunity to make an appropriate strategy for prevention, early diagnosis, and treatment. The most accurate approach for NASH diagnostics is a liver biopsy, which can lead to various complications for the patient. Many papers have studied non-invasive machine learning (ML)-driven approaches to early non-invasive NASH prediction; however, to the best of our knowledge, none of the works considered the problem of explainability of the trained ML models to the medical experts. In this work, we address this issue. We use the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease adult database to train different ML models and propose the technique to explain their predictions. We compare the explanations obtained from a transparent model (Decision Tree) and a non-transparent model (Random Forest). Furthermore, we analyze the quality of explanation prediction by objective means and with a user study involving 11 medical practitioners. Our findings show that there is no significant difference in the perception of explanation obtained from transparent and non-transparent models, and that the explanation of the models’ predictions slightly increases their usability and trustworthiness for real practitioners, enhancing their practical adoption in clinical settings.SpringerUniversidade de Santiago de Compostela. Departamento de Electrónica e Computación20252025-07-2520252025-07-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10347/43406reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860621Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-112623GB-I00 IA RESPONSABLE PARA MINERIA DE PROCESOS 2.0open accesshttp://purl.org/coar/access_right/c_abf2© The Author(s) 2025. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Creative Commons: This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article. To request permission for a type of use not listed, please contact Springer Naturehttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:minerva.usc.gal:10347/434062026-06-15T12:47:27Z
dc.title.none.fl_str_mv On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
title On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
spellingShingle On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
Babakov, Nikolay
Textual explanations
Natural Language Generation
Textual explanations
Explainable Machine Learning
Non-Alcoholic Fatty Liver Disease (NAFLD)
NonAlcoholic SteatoHepatitis (NASH)
title_short On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
title_full On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
title_fullStr On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
title_full_unstemmed On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
title_sort On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis
dc.creator.none.fl_str_mv Babakov, Nikolay
Rezgova, Elena
Reiter, Ehud
Bugarín-Diz, Alberto
author Babakov, Nikolay
author_facet Babakov, Nikolay
Rezgova, Elena
Reiter, Ehud
Bugarín-Diz, Alberto
author_role author
author2 Rezgova, Elena
Reiter, Ehud
Bugarín-Diz, Alberto
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
dc.subject.none.fl_str_mv Textual explanations
Natural Language Generation
Textual explanations
Explainable Machine Learning
Non-Alcoholic Fatty Liver Disease (NAFLD)
NonAlcoholic SteatoHepatitis (NASH)
topic Textual explanations
Natural Language Generation
Textual explanations
Explainable Machine Learning
Non-Alcoholic Fatty Liver Disease (NAFLD)
NonAlcoholic SteatoHepatitis (NASH)
description Nonalcoholic steatohepatitis (NASH) is a common disease that ultimately can lead to the development of end-stage liver disease, cirrhosis, or hepatocellular carcinoma. An early prediction of NASH provides an opportunity to make an appropriate strategy for prevention, early diagnosis, and treatment. The most accurate approach for NASH diagnostics is a liver biopsy, which can lead to various complications for the patient. Many papers have studied non-invasive machine learning (ML)-driven approaches to early non-invasive NASH prediction; however, to the best of our knowledge, none of the works considered the problem of explainability of the trained ML models to the medical experts. In this work, we address this issue. We use the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease adult database to train different ML models and propose the technique to explain their predictions. We compare the explanations obtained from a transparent model (Decision Tree) and a non-transparent model (Random Forest). Furthermore, we analyze the quality of explanation prediction by objective means and with a user study involving 11 medical practitioners. Our findings show that there is no significant difference in the perception of explanation obtained from transparent and non-transparent models, and that the explanation of the models’ predictions slightly increases their usability and trustworthiness for real practitioners, enhancing their practical adoption in clinical settings.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-25
2025
2025-07-25
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10347/43406
url https://hdl.handle.net/10347/43406
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860621
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-112623GB-I00 IA RESPONSABLE PARA MINERIA DE PROCESOS 2.0
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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