Babakov, NikolayRezgova, ElenaReiter, EhudBugarín-Diz, Alberto2025-10-272025-10-272025-07-25Babakov, N., Rezgova, E., Reiter, E., Bugarin Diz, A. (2025). On the role of explanations in machine learning prediction of nonalcoholic steatohepatitis. "Progress in Artificial Intelligence", 2025. https://doi.org/10.1007/s13748-025-00387-82192-6352https://hdl.handle.net/10347/43406Nonalcoholic 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.eng© 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 NatureAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Textual explanationsNatural Language GenerationTextual explanationsExplainable Machine LearningNon-Alcoholic Fatty Liver Disease (NAFLD)NonAlcoholic SteatoHepatitis (NASH)journal article10.1007/s13748-025-00387-82192-6360open access