Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model

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dc.authoridMustafa, Wan Azani/0000-0001-7260-6085
dc.authoridAruchunan, Elayaraja/0000-0002-4629-0483
dc.authoridmadi, elissa nadia/0000-0001-5557-2231
dc.authoridKamil, Anton Abdulbasah/0000-0001-5410-812X
dc.authoridlola, muhamad safiih/0000-0001-9287-7317
dc.contributor.authorNordin, Noor Ilanie
dc.contributor.authorMustafa, Wan Azani
dc.contributor.authorLola, Muhamad Safiih
dc.contributor.authorMadi, Elissa Nadia
dc.contributor.authorKamil, Anton Abdulbasah
dc.contributor.authorNasution, Marah Doly
dc.contributor.authorHamid, Abdul Aziz K. Abdul
dc.date.accessioned2024-09-11T19:53:08Z
dc.date.available2024-09-11T19:53:08Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractSupport ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.en_US
dc.description.sponsorshipUniversiti Malaysia Terengganu (UMT); Unviersiti Malaysia Perlis (UniMAP)en_US
dc.description.sponsorshipThe authors would like to express his gratitude to the Research Management Office (RMO), Universiti Malaysia Terengganu (UMT), and Unviersiti Malaysia Perlis (UniMAP) for partially covering the journal publication fee as well as to the editors and the referees for careful reading and for comments which greatly improved the paper.en_US
dc.identifier.doi10.3390/bioengineering10111318
dc.identifier.issn2306-5354
dc.identifier.issue11en_US
dc.identifier.pmid38002441en_US
dc.identifier.scopus2-s2.0-85178102316en_US
dc.identifier.urihttps://doi.org/10.3390/bioengineering10111318
dc.identifier.urihttps://hdl.handle.net/11363/8076
dc.identifier.volume10en_US
dc.identifier.wosWOS:001107952600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofBioengineering-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectsupport vector machineen_US
dc.subjectlogistic regressionen_US
dc.subjecthybrid modelingen_US
dc.subjectsmall EPV classificationen_US
dc.subjectCOVID-19 predictionen_US
dc.subjectmachine learning classificationen_US
dc.titleEnhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Modelen_US
dc.typeArticleen_US

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