SARS-CoV-2-RBV (1-3)

Published: 18 January 2023| Version 1 | DOI: 10.17632/8hdnzv23x7.1
Mehmet Tahir HUYUT


Only individuals over the age of 18 were included in the study. There are no individual characteristics (name-surname, identification number, etc.) that define the patients in our data set. Routine blood values (RBV) include various biochemical, hematological and immunological parameter properties. The dataset contains 3 files. File 1: SARS-CoV-2-RBV1.sav (IBM SPSS Statistics format). First SARS-CoV-2-RBV1 data set includes the information of 2648 patients diagnosed with COVID-19 and receiving outpatient treatment in hospital on the specified dates, and the same number of patients (control group) whose COVID-19 tests were negative. First column: Diagnosis. In this data set, positive COVID-19 test results were coded as 1 and negative as 0 (COVID-19 = 1, non-COVID-19 = 0). This data set includes 51 RBV features. The first dataset can be used to detect COVID-19. File 2: SARS-CoV-2-RBV2.sav (IBM SPSS Statistics format). This dataset contains information on n = 203 severely infected (ICU) COVID-19 patients and n = 3696 mildly infected (non-ICU) COVID-19 patients. First column: ICU or non-ICU. Severely COVID-19 patients were coded as 1, while mildly COVID-19 patients were coded as 0. This dataset includes 51 RBV features. This dataset can be used for the prognosis/severity of COVID-19. File 3: SARS-CoV-2-RBV3.sav (IBM SPSS Statistics format). SARS-CoV-2-RBV3 dataset includes the information of 233 patients who died in the hospital from COVID-19 and 2364 patients living in the hospital on the specified dates. First column: Contains the patient's output information (survived or non-survived). Patients who died from COVID-19 were coded as 1, and surviving from COVID-19 were coded as 0. This dataset includes 38 RBV features. This dataset can be used for mortality/severity of COVID-19. We grant you a non-exclusive, non-transferable, revocable license to use the SARS-CoV-2-RBV dataset solely for your non-commercial, educational, and research purposes only, but without any right to copy or reproduce, publish or otherwise make available to the public or communicate to the public, sell, rent or lend the whole or any constituent part of the dataset thereof. Please cite the following four articles when you use any of these datasets: 1) Huyut, M.T.; Velichko, A. Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network. Sensors 2022, 22. 2) Huyut, M.T.; Velichko, A.; Belyaev, M. Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers. Appl. Sci. 2022, 12, 12180. 3) Velichko, A.; Huyut, M.T.; Belyaev, M.; Izotov, Y.; Korzun, D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. Sensors 2022, 22, 7886. 4) Huyut, M.T. Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models. IRBM 2022, 1, 1–12.



Erzincan Universitesi Tip Fakultesi


Applied Sciences, Health Sciences, Bioinspired Artificial Intelligence, Computational Intelligence, Machine Learning, Artificial Intelligence Diagnostics, Biological Sciences Research Methodology, Biological Sciences Mathematical Method, COVID-19, COVID-19 Diagnostics