Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/47586
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dc.contributor.authorErgun E.U.-
dc.contributor.authorKok I.-
dc.contributor.authorOzdemir S.-
dc.date.accessioned2023-01-09T21:29:18Z-
dc.date.available2023-01-09T21:29:18Z-
dc.date.issued2022-
dc.identifier.isbn9781665485449-
dc.identifier.urihttps://doi.org/10.1109/ISNCC55209.2022.9851791-
dc.identifier.urihttps://hdl.handle.net/11499/47586-
dc.description2022 International Symposium on Networks, Computers and Communications, ISNCC 2022 -- 19 July 2022 through 21 July 2022 -- 182021en_US
dc.description.abstractData quality plays an important role in increasing the success and reliability of IoT applications. However, due to the nature of IoT, generated data can be missing, erroneous and noisy due to hardware failures, synchronization issues, unstable network communication and manual system closure. Particularly, missing data must be imputed correctly to reduce erroneous or inaccurate decisions in IoT healthcare systems. Therefore, in this paper, we use naive bayes, k-nearest neighbors, decision tree, XGboost algorithms in IoT healthcare domain to reveal the effect of missing data on the results of machine learning algorithms in detail. Then, we make a comparative analysis of the missing data imputation methods. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 International Symposium on Networks, Computers and Communications, ISNCC 2022en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHealthcare systemsen_US
dc.subjectInternet of Thingsen_US
dc.subjectMachine learningen_US
dc.subjectMissing data imputationen_US
dc.subjectDecision treesen_US
dc.subjectHealth careen_US
dc.subjectLearning algorithmsen_US
dc.subjectMachine learningen_US
dc.subjectNearest neighbor searchen_US
dc.subjectComparative analyzesen_US
dc.subjectData qualityen_US
dc.subjectHardware failuresen_US
dc.subjectHealthcare systemsen_US
dc.subjectImputation methodsen_US
dc.subjectMachine-learningen_US
dc.subjectMissing dataen_US
dc.subjectMissing data imputationsen_US
dc.subjectNaive bayesen_US
dc.subjectNetwork communicationsen_US
dc.subjectInternet of thingsen_US
dc.titleImpact of Missing Data on Classification Success in Health and Comparative Analysis of Imputation Methodsen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/ISNCC55209.2022.9851791-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57871640900-
dc.authorscopusid57200283688-
dc.authorscopusid23467461900-
dc.identifier.scopus2-s2.0-85137141499en_US
item.languageiso639-1en-
item.openairetypeConference Object-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept10.10. Computer Engineering-
crisitem.author.dept20.04. Mechatronics Engineering-
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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