
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 7 Issue 4
July-August 2025
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AI-Based Stroke Prediction Using Machine Learning: A Comparative Model Evaluation with SHAP Explainability
Author(s) | Ms. Chitra Devi Thangavelu, Mr. Gnanaguru PP, Mr. Arunkumar VL, Mr. Abhilash Joshua M |
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Country | India |
Abstract | Stroke is a major global health concern and a leading cause of mortality and long-term disability. Early detection through predictive modeling can significantly improve clinical outcomes and reduce the burden on healthcare systems. This study presents a comprehensive machine learning approach to stroke prediction using clinical data. Three classifiers Random Forest, XGBoost, and Logistic Regression were implemented and evaluated based on accuracy, AUC, and confusion matrices. SHAP (Shapley Additive explanations) was employed to interpret the model decisions. Among the models, XGBoost demonstrated the highest AUC. SHAP analysis revealed that age, average glucose level, and BMI were key contributing features. This research underlines the potential of explainable AI in enhancing medical decision-making. |
Keywords | Stroke, Machine Learning, SHAP, XGBoost, Random Forest, Logistic Regression, Predictive Modeling, Explainable AI |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 7, Issue 4, July-August 2025 |
Published On | 2025-07-22 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i04.51727 |
Short DOI | https://doi.org/g9t2dj |
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E-ISSN 2582-2160

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IJFMR DOI prefix is
10.36948/ijfmr
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