Analyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID-19

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dc.authoridYucelbas, Sule/0000-0002-6758-8502
dc.authoridErol Dogan, Gizemnur/0000-0001-9347-9775
dc.contributor.authorErol, Gizemnur
dc.contributor.authorUzbas, Betul
dc.contributor.authorYucelbas, Cuneyt
dc.contributor.authorYucelbas, Sule
dc.date.accessioned2025-03-17T12:27:46Z
dc.date.available2025-03-17T12:27:46Z
dc.date.issued2022
dc.departmentTarsus Üniversitesi
dc.description.abstractReal-time polymerase chain reaction (RT-PCR) known as the swab test is a diagnostic test that can diagnose COVID-19 disease through respiratory samples in the laboratory. Due to the rapid spread of the coronavirus around the world, the RT-PCR test has become insufficient to get fast results. For this reason, the need for diagnostic methods to fill this gap has arisen and machine learning studies have started in this area. On the other hand, studying medical data is a challenging area because the data it contains is inconsistent, incomplete, difficult to scale, and very large. Additionally, some poor clinical decisions, irrelevant parameters, and limited medical data adversely affect the accuracy of studies performed. Therefore, considering the availability of datasets containing COVID-19 blood parameters, which are less in number than other medical datasets today, it is aimed to improve these existing datasets. In this direction, to obtain more consistent results in COVID-19 machine learning studies, the effect of data preprocessing techniques on the classification of COVID-19 data was investigated in this study. In this study primarily, encoding categorical feature and feature scaling processes were applied to the dataset with 15 features that contain blood data of 279 patients, including gender and age information. Then, the missingness of the dataset was eliminated by using both K-nearest neighbor algorithm (KNN) and chain equations multiple value assignment (MICE) methods. Data balancing has been done with synthetic minority oversampling technique (SMOTE), which is a data balancing method. The effect of data preprocessing techniques on ensemble learning algorithms bagging, AdaBoost, random forest and on popular classifier algorithms KNN classifier, support vector machine, logistic regression, artificial neural network, and decision tree classifiers have been analyzed. The highest accuracies obtained with the bagging classifier were 83.42% and 83.74% with KNN and MICE imputations by applying SMOTE, respectively. On the other hand, the highest accuracy ratio reached with the same classifier without SMOTE was 83.91% for the KNN imputation. In conclusion, certain data preprocessing techniques are examined comparatively and the effect of these data preprocessing techniques on success is presented and the importance of the right combination of data preprocessing to achieve success has been demonstrated by experimental studies.
dc.identifier.doi10.1002/cpe.7393
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue28
dc.identifier.pmid36714180
dc.identifier.scopus2-s2.0-85140036970
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/cpe.7393
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2416
dc.identifier.volume34
dc.identifier.wosWOS:000869547800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofConcurrency and Computation-Practice & Experience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectCOVID-19
dc.subjectKNN imputation
dc.subjectmachine learning
dc.subjectmultivariate imputation by chained equation
dc.subjectsynthetic minority oversampling technique
dc.titleAnalyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID-19
dc.typeArticle

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