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.
Keywords
Multimodal AI, Fake News Detection, Misinfor- Mation, Bi-LSTM, CNN, Web Intelligence, Social Media Analytics.
Citations
IRE Journals:
Gurusidda, Vinayak M Naganur, Sanjeev Reddy, Mithun Gowda SR, Ganesh A P; Pradeep Nazareth "Brain Stroke Prediction Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1506-1514 https://doi.org/10.64388/IREV9I6-1713022
IEEE:
Gurusidda, Vinayak M Naganur, Sanjeev Reddy, Mithun Gowda SR, Ganesh A P; Pradeep Nazareth
"Brain Stroke Prediction Using Machine Learning" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1713022
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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