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Abstract— Stroke represents a significant global health challenge, characterized by high rates of mortality and long-term disability. This paper introduces an enhanced machine-learning-based approach for the detection of brain stroke risk. The proposed system leverages patient medical history, demographic, and lifestyle data to predict stroke likelihood, thereby facilitating early diagnosis and intervention. A key contribution of this work is the development of an end-to-end smart healthcare system, incorporating a user-friendly interface and a focus on explainability. The system addresses the inherent class imbalance present in stroke datasets through a hybrid approach, combining oversampling and undersampling techniques. The predictive performance of several machine learning models is evaluated and compared.
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"Brain Stroke Risk Detection Using Machine Learning: An Enhanced Approach", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c422-c426, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504271.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator