Explainable AI for Stroke Prediction: A Comprehensive Approach to Enhancing Transparency and Interpretability

Explainable AI for Stroke Prediction: A Comprehensive Approach to Enhancing Transparency and Interpretability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Explainable AI for Stroke Prediction: A Comprehensive Approach to Enhancing Transparency and Interpretability Marwa El-Geneedy, Hossam Moustafa, Hatem Khater, Seham Abd-Elsamee, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5950552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version 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 will help medical practitioners to effectively manage their patients and develop a screening tool that incorporates technologies to revolutionize the approach to preventing and treating stroke. Health sciences/Health care Physical sciences/Engineering/Biomedical engineering Biological sciences/Computational biology and bioinformatics/Biochemical reaction networks Biological sciences/Computational biology and bioinformatics/Cellular signalling networks Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data acquisition Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Software Stroke Explainable Artificial Intelligence (XAI) SHAP LIME ELI5 Classification Feature Selection Full Text Additional Declarations No competing interests reported. Supplementary Files LatexBundle.zip Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 01 May, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 28 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 20 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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