Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shobana, V.; Maheshwari, S.; Savithri, M.; Ramasamy, Siva Shankar; Kumar, N. Title A name given to the resource Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025 - Proceedings;pp.461-469 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMLAS64557.2025.10969073" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMLAS64557.2025.10969073</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004811323?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004811323?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shobana V., Dr. N.G.P. Arts and Science College, Department of Computer Science with Cyber Security, Coimbatore, India; Maheshwari S., Dr. N.G.P. Arts and Science College, Department of Computer Science, Coimbatore, India; Savithri M., Christ University, Department of Data Science, Bengaluru, India; Ramasamy S.S., International College of Digital Innovation, Chiang Mai University, Thailand; Kumar N., Dr. N.G.P. Arts and Science College, Department of Computer Science, Coimbatore, India Description An account of the resource Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE. Subject The topic of the resource Brain Stroke; Explainable Artificial Intelligence (XAI); Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150574-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online