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Diagnosis and Prognosis of COVID‐19 Disease Using Routine Blood Values and LogNNet Neural NetworkHuyut, Mehmet TahirVelichko, Andreibiochemical and hematological biomarkersCOVID‐19feature selection methodInternet of Medical ThingsIoTLogNNet neural networkroutine blood valuesCOVID-19HumansNeural Networks, ComputerPandemicsPrognosisSARS-CoV-2BloodBlood pressureDiagnosisHemoglobinInternet of thingsPatient treatmentStatistical testsAccuracy rateBiochemical and hematological biomarkerDiagnosis and prognosisFeature selection methodsHealth systemsInternet of medical thingNeural-networksRoutine blood valuehumanpandemicSince February 2020, the world has been engaged in an intense struggle with the COVID‐19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID‐19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID‐19 tests. The LogNNet‐model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and acti-vated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID‐19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neu-trophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID‐19 using the key fea-tures. The method is promising to create mobile health monitoring systems in the Internet of Things. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.MDPI2022Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/12961https://doi.org/10.3390/s221348201424-8220enSensorsinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/12961