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A Systematic Review of Machine Learning in Substance Addiction

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dc.citedby2
dc.contributor.authorZulkifli N.F.en_US
dc.contributor.authorCob Z.C.en_US
dc.contributor.authorLatif A.A.en_US
dc.contributor.authorDrus S.M.en_US
dc.contributor.authorid57220808082en_US
dc.contributor.authorid25824919900en_US
dc.contributor.authorid46461488000en_US
dc.contributor.authorid56330463900en_US
dc.date.accessioned2023-05-29T08:08:26Z
dc.date.available2023-05-29T08:08:26Z
dc.date.issued2020
dc.descriptionPatient treatment; Healthcare industry; Machine learning methods; Open doors; Systematic Review; Machine learningen_US
dc.description.abstractSubstance addiction affects millions of people worldwide and there is no cure for addiction. With the emergence of machine learning, it has open doors for healthcare industry to incorporate technology to help healthcare workforce to make better decision in treating patients. By applying machine learning in understanding patients with substance addiction, it can help in determining their treatment. This paper aims to provide a summary of how effective machine learning method is applied in addiction studies in which 11 studies are included in this paper by using PRISMA methodology to find sources. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243581
dc.identifier.doi10.1109/ICIMU49871.2020.9243581
dc.identifier.epage107
dc.identifier.scopus2-s2.0-85097639829
dc.identifier.spage103
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097639829&doi=10.1109%2fICIMU49871.2020.9243581&partnerID=40&md5=7542b31ea6cbd200ae7ae78a975cbdb3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25352
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleA Systematic Review of Machine Learning in Substance Addictionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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