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....
| Autores: | , , , |
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| 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) |
| Sumario: | 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. |
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