Year: 2025
Type: article
Abstract: This review delves deeply into deep learning tech- niques for brain stroke prediction. It focuses on real-time Internet of Medical Things (IoMT) systems and mixed architectures of convolutional and recurrent neural networks. The study looks at preprocessing, feature selection, model clarity, and ways to boost prediction accuracy. It also explores how big medical datasets, transfer learning, and real-time analysis help build strong stroke prediction models. The paper examines several cutting-edge frameworks to see how well they work for early diagnosis and decision support. Deep learning algorithms can spot subtle patterns in brain scans and patient records that normal stats often miss. Also, using cloud computing, edge AI, and mobile health platforms allows for quick stroke risk checks in far-off or underserved areas. The paper reviews explainable AI techniques to increase trust in clinics and talks about ethical issues like data privacy and fairness. It analyzes challenges recent progress, and future paths to create efficient, accurate, and usable stroke prediction systems.
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7116796607