Abstract: Brain Stroke prediction introduces a robust, multi-modal machine learning system engineered to precisely forecast brain stroke risk by integrating two fundamentally different data sources: conventional, structured clinical data and complex, unstructured CT/MRI neuroimaging. The system is built upon a dual-stream architecture: the gradient boosting algorithm XGBoost is deployed to analyze the patient's record features (e.g., demographics and history), and a deep convolutional network, EfficientNet-B0, is dedicated to extracting visual pathological indicators from the brain scans. A core objective is to ensure system trustworthiness through the application of Explainable AI (XAI), specifically SHAP (Shapley Additive Explanations), which guarantees clarity and interpretability for medical professionals. This scalable solution marks a significant advancement in early stroke detection and enables evidence- based clinical decision support.
Keywords: Explainable AI (XAI), SHAP (Shapley Additive Explanations) , EfficientNet-B0, XGBoost, Convolutional Neural Network (CNN).
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DOI:
10.17148/IJARCCE.2025.141259
[1] Mrs. R S Geethanjali, M Sowmya, M Meghana, and R Prudvi Ganesh, "Brain Stroke Prediction," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141259
