AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation

Marta Narigina, Agris Vindecs, Dušanka Bošković, Yuri Merkuryev and Andrejs Romanovs ()
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Marta Narigina: Institute of Information Technology, Riga Technical University, 6A Kipsalas Street, LV-1048 Riga, Latvia
Agris Vindecs: Institute of Information Technology, Riga Technical University, 6A Kipsalas Street, LV-1048 Riga, Latvia
Dušanka Bošković: Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne bb, 71000 Sarajevo, Bosnia and Herzegovina
Yuri Merkuryev: Institute of Information Technology, Riga Technical University, 6A Kipsalas Street, LV-1048 Riga, Latvia
Andrejs Romanovs: Institute of Information Technology, Riga Technical University, 6A Kipsalas Street, LV-1048 Riga, Latvia

Future Internet, 2025, vol. 17, issue 5, 1-27

Abstract: This study introduces an AI-based framework for stroke diagnosis that merges clinical data and curated imaging data. The system utilizes traditional machine learning and advanced deep learning techniques to tackle dataset imbalances and variability in stroke presentations. Our approach involves rigorous data preprocessing, feature engineering, and ensemble techniques to optimize the predictive performance. Comprehensive evaluations demonstrate that gradient-boosted models outperform in accuracy, while CNNs enhance stroke detection rates. Calibration and threshold optimization are utilized to align predictions with clinical requirements, ensuring diagnostic reliability. This multi-modal framework highlights the capacity of AI to accelerate stroke diagnosis and aid clinical decision making, ultimately enhancing patient outcomes in critical care.

Keywords: stroke diagnosis; eICU database; machine learning; deep learning; convolutional neural networks; ensemble methods; calibration; threshold optimization (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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