Machine Learning for Brain Stroke: A Review |
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| Affiliation: | 2. University of Madeira, Rua Dos Ferreiros 105, Funchal 9000-082 Portugal;3. University of Coimbra, Rua do Colégio Novo, 3000-115, Coimbra;1. Royal Adelaide Hospital, Australia;2. Faculty of Health and Medical Sciences, University of Adelaide, Australia;3. Australian Institute for Machine Learning, University of Adelaide, Australia;1. School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China;2. Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;3. Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China;4. Department of Geriatrics, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;5. Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;6. Department of Cardiovascular Medicine, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China;7. Division of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;1. Department of Internal Medicine, University of Thessaly, Greece;2. Department of Digital Systems, University of Piraeus, Greece;3. Department of Clinical Therapeutics, University of Athens, Greece;4. Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece |
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| Abstract: | Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke. |
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