Publication:
Machine Learning Algorithms for Diabetes Prediction: A Review Paper

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dc.citedby2
dc.contributor.authorAl-Sideiri A.en_US
dc.contributor.authorCob Z.B.C.en_US
dc.contributor.authorDrus S.B.M.en_US
dc.contributor.authorid57207830966en_US
dc.contributor.authorid25824919900en_US
dc.contributor.authorid56330463900en_US
dc.date.accessioned2023-05-29T07:22:24Z
dc.date.available2023-05-29T07:22:24Z
dc.date.issued2019
dc.descriptionComputer aided diagnosis; Data mining; Learning systems; Patient treatment; Predictive analytics; Robotics; Support vector machines; Data mining algorithm; Diabetes mellitus; Early diagnosis; Knowledge accumulation; Literature reviews; Prediction techniques; Review papers; Support vector machine algorithm; Learning algorithmsen_US
dc.description.abstractThe early diagnosis of the diabetes disease is a very important for cure process, and that provides an ease process of treatment for both the patient and the doctor. At this point, statistical methods and data mining algorithms can provide significance chances for early diagnosis of diabetes mellitus (DM). In the literature, many studies have been published for solution of this problem. Initially, these studies are analyzed in detail and classified according to their methodologies. The main aim of this paper is to provide the comprehensive and detailed review of the diagnosis of diabetes by machine learning algorithms. Also, this paper presents a literature review on the diagnosis diabetes up to the mid of 2019. This paper provides to guide future research and knowledge accumulation and creation of classification and prediction techniques in diagnosis of diabetes. This study shows that the Support Vector Machine (SVM) algorithm is the most used machine learning algorithms and it provide more accurate and powerful results. � 2019 ACM.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1145/3388218.3388231
dc.identifier.epage32
dc.identifier.scopus2-s2.0-85086183023
dc.identifier.spage27
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086183023&doi=10.1145%2f3388218.3388231&partnerID=40&md5=8bb6d9318ec1871f72e6cb95610c189e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24242
dc.publisherAssociation for Computing Machineryen_US
dc.sourceScopus
dc.sourcetitleACM International Conference Proceeding Series
dc.titleMachine Learning Algorithms for Diabetes Prediction: A Review Paperen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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