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Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning ModelsHuyut, M.T.Biochemical and hematological biomarkersClassificationCOVID-19Feature selection methodsRoutine blood valuesSupervised machine learning modelshemoglobinadultagedarea under the curveArticleBayesian learningblood cell countcapillary flowcontrolled studycoronavirus disease 2019demographicsdisease severityeosinophil counterythrocyte counterythrocyte sedimentation ratefeature extractionfemaleflow kineticshematocrithematological parametershemoglobin determinationhospital admissionhospital patienthumanObjectives: When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission. Material and methods: The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set. Results: In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%. Conclusion: The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients. © 2022 AGBMElsevier Masson s.r.l.2023Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/12323https://doi.org/10.1016/j.irbm.2022.05.0061959-0318enIRBMinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12323