## 1 Introduction
In contrast, machine learning (ML) models have been successfully used to recognize subtle patterns in data to distinguish latent-association patterns between routine blood-parameters and disease [3; 13; 22; 31; 32]. Indeed, several studies have shown that ML models can predict COVID-19 patient groups with high accuracy, using patient demographics, physiological characteristics, and RBV data [33; 34; 35; 36]. In our previous studies, we determined routine blood-values that predict the diagnosis and prognosis of COVID-19 with various supervised ML models and LogNNet [3; 4; 13; 22; 32]. [PERSON] and [PERSON] [22] used only three RBV features with the LogNNet model to predict the diagnosis and prognosis of COVID-19 disease. They achieved an accuracy of 99.17% in the diagnosis of the disease and an accuracy of 82.7% in determining the prognosis of the disease. [PERSON] et al. [32] achieved 100% accuracy in the diagnosis of COVID-19, using 11 RBV features with a histogram-based gradient-boosting model in their study. [PERSON] [3] classified severe and mild COVID patients from a large patient population, using 28 RBV features, and the models with the highest classification accuracy were locally weighted learning (97.86%) and k-nearest neighbor (94.05%). [PERSON] and [PERSON] [4] used various biomarkers with the CHAID decision tree to detect the diagnosis and prognosis of COVID-19. The model showed 81.6% accuracy in recognizing the disease and 93.5% accuracy in determining the prognosis of the disease. [PERSON] et al. [37] developed an ML model for early diagnosis of disease, using eight RBVfeatures, and reported an 82% specificity with 83% sensitivity; however, the analysis was based on a small sample (171 patients). [PERSON] et al. [38] classified a patient cohort of 598 cases, 39 of whom were COVID-19 positive, by various ML methods, using 12 RBV features, and reported good specificity (91%) but very low sensitivity (43%). [PERSON] et al. [39] developed a Bayesian model using the dataset of 12 RBV features, and reported a sensitivity and specificity of 76.7% in the diagnosis of the disease. [PERSON] et al. [40] developed a trained logistic-regression model using only hemogram data on a dataset of 380 cases, and reported 93% sensitivity but low 43% specificity in the diagnosis of COVID-19 disease. [PERSON] et al. [41] used 78 features, consisting of demographic, clinical, and RBV values, with a deep neural network model to predict the mortality of COVID-19. They found the success of the method in diagnosis to be 95.4% AUC. [PERSON] et al. [42] ran models of multivariate logistic regression, random forests and extreme gradient-supported trees on more than 50 RBV data, to identify COVID-19. The most successful model in the diagnosis of the disease was the XGBoost method, with 85% sensitivity and 90% accuracy. [PERSON] [43] developed an ML model using 15 RBV parameters to diagnose COVID-19 on a sample of 599 people, 81 of whom were COVID-19-positive. Combining the SVM, ensemble, and SMOTE Boost models, this model had 86% specificity and 70% success in diagnosing the disease. In this study, 34 routine blood-values were determined with a statistical approach, in order to find the most successful ML classifier-model in detecting patients with COVID-19 who lived and died. Therefore, 16 ML models were run with these features, and the most successful model (histogram-based gradient-boosting/HGB) was determined. The predictors of mortality of COVID-19 disease were revealed with the HGB model. The performance of individual and binary combinations of these predictors in detecting patient groups was obtained with the HGB model. In addition, correlations between patient groups and binary combinations of features were interpreted in detail. In addition, cut-off values for these characteristics (mortality risk-levels) were calculated using one- and two-threshold approaches in the classification of patient groups according to direct characteristics.