Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9930
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dc.contributor.authorPratheeba, J.-
dc.date.accessioned2023-12-15T06:02:21Z-
dc.date.available2023-12-15T06:02:21Z-
dc.date.issued2023-
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9930-
dc.description.abstractSARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naive Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methodsen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectCOVID-19 diagnosisen_US
dc.subjectFeature selectionen_US
dc.subjectTranscriptome dataen_US
dc.subjectMachine learning modelsen_US
dc.subjectDifferently expressed genesen_US
dc.subjectGO analysisen_US
dc.titleSARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approachen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/l O. 1007/s42979-023-017036en_US
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