PREDICTION OF COVID-19 SEVERITY IN SARS-COV-2 RNA-POSITIVE PATIENTS BY DIFFERENT ENSEMBLE LEARNING STRATEGIES

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dc.authoridGozukara Bag, Harika Gozde/0000-0003-1208-4072
dc.authoridÇOLAK, CEMİL/0000-0001-5406-098X
dc.authorwosidGozukara Bag, Harika Gozde/ABG-7588-2020
dc.authorwosidÇOLAK, CEMİL/ABI-3261-2020
dc.contributor.authorBag, Harika Gozde Gozukara
dc.contributor.authorKivrak, Mehmet
dc.contributor.authorGuldogan, Emek
dc.contributor.authorColak, Cemil
dc.date.accessioned2024-08-04T21:00:11Z
dc.date.available2024-08-04T21:00:11Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction: While the coronavirus only persists marginally for 95% of the infected cases, the remaining 5% are in critical or life-threatening conditions. This study aimed to design an intelligent model that predicts the severity level of the disease by modeling the relationships between the COVID-19 infection severity and the various demographic/clinical features of individuals. Materials and methods: A public dataset of a cross-sectional study including the demographic and symptomatological characteristics of 223 COVID-19 patients was used and randomly partitioned into training (75%) and testing (25%) datasets. During training, the class imbalance problem was solved, and the related factors with the COVID-19 severity were selected using the evolutionary method supported by a genetic algorithm. Neural Network (NN), Support Vector Machine (SVM), QUEST algorithms together with confidence weighted voting, voting, and highest confidence wins strategies (HCWS) were constructed, and the predictive power of models was determined by performance metrics. Results: Based on the performance indicators, among the individual models, the NN model outperformed SVM and QUEST algorithms in the training and testing datasets. However, ensemble approaches gave better predictions as compared to individual models according to all the evaluation metrics. Conclusion: The proposed voting ensemble model outperforms other ensemble and individual machine learning approaches for the severity prediction of COVID-19 disease. The proposed ensemble learning model can be integrated into web or mobile applications to classify the severity of COVID-19 for clinical decision support.en_US
dc.identifier.doi10.19193/0393-6384_2022_2_166
dc.identifier.endpage1091en_US
dc.identifier.issn0393-6384
dc.identifier.issn2283-9720
dc.identifier.issue2en_US
dc.identifier.startpage1085en_US
dc.identifier.urihttps://doi.org/10.19193/0393-6384_2022_2_166
dc.identifier.urihttps://hdl.handle.net/11616/103880
dc.identifier.volume38en_US
dc.identifier.wosWOS:000798951300050en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherCarbone Editoreen_US
dc.relation.ispartofActa Medica Mediterraneaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectCOVID-19 severityen_US
dc.subjectensemble learningen_US
dc.subjectmachine learningen_US
dc.subjectpredictionen_US
dc.titlePREDICTION OF COVID-19 SEVERITY IN SARS-COV-2 RNA-POSITIVE PATIENTS BY DIFFERENT ENSEMBLE LEARNING STRATEGIESen_US
dc.typeArticleen_US

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