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Mohammed Sakib Hasan: We Propose Innovative AI-Driven Solutions for Real-Time Stroke Prediction
Mohammed Sakib Hasan/LinkedIn
May 31, 2026, 10:03

Mohammed Sakib Hasan: We Propose Innovative AI-Driven Solutions for Real-Time Stroke Prediction

Mohammed Sakib Hasan, Assistant General Secretary (Academic Research And Higher Studies) at CSE Club, PSTU, shared on LinkedIn about a recent article he and his colleagues co-authored, published in IEEE Xplore, adding:

”Alhamdulillah!

Our research paper, ‘StrokeGuardian: Hybrid Ensemble ML for Real-Time Brain Stroke Prediction and Clinical Decision Support,’ has been published at IEEE WIECON-ECE 2025.

Our proposed hybrid ensemble model combines Neural Networks, SVM, and XGBoost to deliver highly accurate and explainable stroke risk prediction, achieving 99.15% accuracy and 0.9979 ROC-AUC.

The system also includes a cloud-based clinical decision support platform for real-time healthcare applications.”

Title: StrokeGuardian: Hybrid Ensemble ML for Real-Time Brain Stroke Prediction and Clinical Decision Support

Authors: Mohammed Sakib Hasan, Abdul Jobayer, Jihadul Islam, Sanjida Islam Nuha, Golam Muradul Bashir

Mohammed Sakib Hasan: We Propose Innovative AI-Driven Solutions for Real-Time Stroke Prediction

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Abdul Jobayer AI Golam Muradul Bashir Health Hematology Hemostasis Hemostasis News Hemostasis Today IEEE WIECON-ECE 2025 Jihadul Islam Medicine Mohammed Sakib Hasan Neural Networks Sanjida Islam Nuha stroke stroke risk prediction SVM thrombosis XGBoost
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