Analyzing longitudinal data using machine learning with mixed-effects models

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dc.authorid0000-0002-5919-1986
dc.contributor.authorYiğit, Pakize
dc.contributor.authorAhmed, Syed Ejaz
dc.date.accessioned2024-11-07T09:14:55Z
dc.date.available2024-11-07T09:14:55Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyoistatistik ve Tıp Bilişimi Ana Bilim Dalı
dc.description.abstractThe study evaluates the performances of two mixed-effect machine learning methods (RE-EM trees and MERF) and mixed effects penalized methods, three classical machine learning methods (random forest, gradient boosting trees, support vector machine), penalized and shrinkage models (Ridge, LASSO, ALASSO, SCAD, shrinkage) in the prediction of high dimensional longitudinal data. It aims to compare mixed effect models and traditional models’ prediction accuracy using correlated data. Two real data applications are used aiming to predict the COVID-19 number of cases and pandemic-related number of deaths. The models are evaluated according to the RMSE values of the predictors.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)en_US
dc.identifier.citationYiğit, P. ve Ahmed, S. E. (2024). Analyzing longitudinal data using machine learning with mixed-effects models. Eighteenth International Conference On Management Science And Engineering Management, Icmsem 2024, 215, 633-646. Kuala Lumpur, Malaysia, August 5-8, 2024. http://dx.doi.org/10.1007/978-981-97-5098-6_44
dc.identifier.doi10.1007/978-981-97-5098-6_44
dc.identifier.endpage646
dc.identifier.isbn9789819750979
dc.identifier.isbn9789819750986
dc.identifier.issn2367-4512
dc.identifier.scopus2-s2.0-85201313029
dc.identifier.scopusqualityQ3
dc.identifier.startpage633
dc.identifier.urihttp://dx.doi.org/10.1007/978-981-97-5098-6_44
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12872
dc.identifier.volume215
dc.identifier.wos001323495500044en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYiğit, Pakize
dc.language.isoen
dc.relation.ispartofEighteenth International Conference On Management Science And Engineering Management, Icmsem 2024en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/2219
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDecision Trees
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectStatistical Models
dc.subjectSupport Vector Regression
dc.titleAnalyzing longitudinal data using machine learning with mixed-effects models
dc.typeConference Object

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