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Machine learning for brain stroke: a review

2020Journal article Open access
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dc.contributor.authorSirsat, Manisha Sanjay
dc.contributor.authorFermé, Eduardo
dc.contributor.authorCâmara, Joana
dc.date.accessioned2021-05-26T14:20:06Z
dc.date.available2021-05-26T14:20:06Z
dc.date.issued2020
dc.description.abstractMachine 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSirsat, M. S., Fermé, E., & Câmara, J. (2020). Machine learning for brain stroke: a review. Journal of Stroke and Cerebrovascular Diseases, 29(10), 105162.pt_PT
dc.identifier.doi10.1016/j.jstrokecerebrovasdis.2020.105162pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/3438
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSupport vector machinept_PT
dc.subjectMachine learningpt_PT
dc.subjectDeep learningpt_PT
dc.subjectStroke diagnosispt_PT
dc.subjectStroke preventionpt_PT
dc.subjectStroke prognosticationpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleMachine learning for brain stroke: a reviewpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-COM%2F30990%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F04516%2F2019/PT
oaire.citation.issue10pt_PT
oaire.citation.startPage105162pt_PT
oaire.citation.titleJournal of Stroke and Cerebrovascular Diseasespt_PT
oaire.citation.volume29pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFermé
person.familyNameCâmara
person.givenNameEduardo
person.givenNameJoana
person.identifierhttps://scholar.google.com/citations?user=Qu6aCR8AAAAJ&hl
person.identifier.ciencia-idC515-D92F-8F54
person.identifier.ciencia-id4B13-EBC6-4581
person.identifier.orcid0000-0002-9618-2421
person.identifier.orcid0000-0002-4330-7702
person.identifier.scopus-author-id6507544015
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication85e9c7c7-51bc-42cd-b1f5-f72f10fe02d2
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