ItemConference Paper

Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention

Title
Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Creator
Shobana, V.; Maheshwari, S.; Savithri, M.; Ramasamy, Siva Shankar; Kumar, N.
Description
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.
Source
2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025 - Proceedings;pp.461-469
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Brain Stroke; Explainable Artificial Intelligence (XAI); Machine Learning
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833150574-5;
Format
online
Language
English
Type
Conference paper

Citation

Shobana, V.; Maheshwari, S.; Savithri, M.; Ramasamy, Siva Shankar; Kumar, N., “Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26060.

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