Sawan, AkthamAwad, MohammedQasrawi, Radwan2023-10-222023-10-222023Sawan, A., Awad, M., & Qasrawi, R. (2023, May). Classification of Stroke Using Machine Learning Techniques: Review Study. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-8). IEEE.9798350347074https://doi.org/10.1109/ICCAD57653.2023.10152317https://hdl.handle.net/20.500.12713/3987Abstract—Presently, stroke is the leading cause of adult injury worldwide. The World Health Organization estimates that each year 15 million people around the world suffer a stroke. Five million of them die, and another five million are disabled for life. There is a chance to dramatically enhance the classification of strokes in the early stages. In this article, we reviewed all portable devices that produced electroencephalogram(EEG) data and all machine learning (ML) methods and deep-learning methods used to identify stroke using EEG data, and we noted that the amount of work on ML and deep learning in analyzing EEG data have increased rapidly in recent years. Such analysis has achieved greater precision compared to that conventional methods. We also discussed in this study the opportunities and key challenges for improving the accuracy of future work.eninfo:eu-repo/semantics/closedAccessDeep learning , Imaging , Organizations , Stroke (medical condition) , Brain modeling , Electroencephalography , Real-time systemsDeep LearningImagingOrganizationsStroke (Medical Condition)Brain ModelingElectroencephalographyReal-Time SystemsConference Object3312-s2.0-8516413024010.1109/ICCAD57653.2023.10152317N/A