A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction
Abstract Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessa...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11263-9 |
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| author | Marwa El-Geneedy Hossam El-Din Moustafa Hatem Khater Seham Abd-Elsamee Samah A. Gamel |
| author_facet | Marwa El-Geneedy Hossam El-Din Moustafa Hatem Khater Seham Abd-Elsamee Samah A. Gamel |
| author_sort | Marwa El-Geneedy |
| collection | DOAJ |
| description | Abstract Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML). In this study, we used ML classifiers and explainable artificial intelligence (XAI) to predict stroke. Six different ML classifiers that trained on available datasets for stroke patients. Six feature selection methodologies were used to extract essential features from the dataset. The XAI methods applied (Shapley Additive Values (SHAP), ELI5, and Local Interpretable Model-agnostic Explanations (LIME)). This study provides preliminary insights that may support the development of future tools to assist medical practitioners in managing patients, pending further clinical validation and real-world testing. |
| format | Article |
| id | doaj-art-7d3bc605a4a5400b9e1ccc185b50a7dc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7d3bc605a4a5400b9e1ccc185b50a7dc2025-08-20T03:45:59ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-11263-9A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke predictionMarwa El-Geneedy0Hossam El-Din Moustafa1Hatem Khater2Seham Abd-Elsamee3Samah A. Gamel4Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura UniversityElectronics and Communications Engineering Department, Faculty of Engineering, Mansoura UniversityElectrical Department, Faculty of Engineering, Horus University EgyptElectronics and Communications Engineering Department, Faculty of Engineering, Mansoura UniversityElectrical Department, Faculty of Engineering, Horus University EgyptAbstract Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML). In this study, we used ML classifiers and explainable artificial intelligence (XAI) to predict stroke. Six different ML classifiers that trained on available datasets for stroke patients. Six feature selection methodologies were used to extract essential features from the dataset. The XAI methods applied (Shapley Additive Values (SHAP), ELI5, and Local Interpretable Model-agnostic Explanations (LIME)). This study provides preliminary insights that may support the development of future tools to assist medical practitioners in managing patients, pending further clinical validation and real-world testing.https://doi.org/10.1038/s41598-025-11263-9StrokeExplainable Artificial Intelligence (XAI)SHAPLIMEELI5Classification |
| spellingShingle | Marwa El-Geneedy Hossam El-Din Moustafa Hatem Khater Seham Abd-Elsamee Samah A. Gamel A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction Scientific Reports Stroke Explainable Artificial Intelligence (XAI) SHAP LIME ELI5 Classification |
| title | A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction |
| title_full | A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction |
| title_fullStr | A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction |
| title_full_unstemmed | A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction |
| title_short | A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction |
| title_sort | comprehensive explainable ai approach for enhancing transparency and interpretability in stroke prediction |
| topic | Stroke Explainable Artificial Intelligence (XAI) SHAP LIME ELI5 Classification |
| url | https://doi.org/10.1038/s41598-025-11263-9 |
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