## 3 Results
### Investigation of the Effectiveness of the Models Operating on the One-Feature HGB Model
#### 3.4.2 Comparison of Spearman Correlation and HGB Model and Threshold Approach  
It was observed that the performance of the HGB model with a single feature (F1\({}^{2}\)) in the classification of patient groups who died from and survived COVID-19 was more successful than the classification made by considering one- and two-threshold values (Figure 11). In addition, [PERSON]'s correlation gave a similar distribution of features in terms of importance, as the presented models. Figure 10: Histogram distributions and F1\({}^{2}\) results of amylase feature according to two-threshold value approach in estimating COVID-19 mortality. \(V_{\text{th,-1}}\) (pink line) and \(V_{\text{th,-2}}\) (blue line) is the threshold for detecting COVID-19 mortality. Figure 9: Histogram distributions and F1\({}^{2}\) results of (**a**) procalcitonin, (**b**) ferritin and (**c**) fibrinogen properties, according to the single-cut-off value approach in estimating COVID-19 mortality. \(V_{\text{th}}\) (blue line) is the threshold for detecting COVID-19 mortality. Investigation of the Effectiveness of the HGB Model Working on Two Features for the Detection of Surviving and Non-Surviving COVID-19  
For the detection of living and deceased COVID-19 patient-groups, the SMOTE-trained HGB model was run with dual features, and classification performances are presented in Table 4. In addition, F12 values related to binary properties and classification performances operated with the HGB model are visualized in two-dimensional space (Figure 12). Figure 11: F12 metric of SARS-CoV-2-RBV3 dataset for different models. When Table 6 and Figure 12 are examined, the HGB model shows a classification performance of F1\({}^{2}\) = 0.98 with only D-dimer and PCT feature pair, in the detection of surviving and non-surviving patients. The classification performances of the feature pairs formed by PCT with ESR, D-Bil, Ferritin, and LDH were approximately F1\({}^{2}\) = 0.98. Both surviving and non-surviving patients with these feature pairs were identified, with high precision and recall values. PCT appears to be the feature that most closely matches other features in predicting disease mortality. After PCT, it can be said that ferritin is the most-matching property with other properties. In addition, \(\geq\) 0.94 F1\({}^{2}\) values were found in the patient-group classification of various feature pairs with the HGB model (Table 6)  
In addition, according to the two-threshold approach, it was found that the majority of patients who died had fibrinogen values between 349.98 g/L and 379.05 g/L (F1\({}^{2}\) = 0.68), D-dimer values between 1009.99 \(\upmu\)g/L and 10,742.71 \(\upmu\)g/L (F1\({}^{2}\) = 0.65), ESR values between 36.12 and 56.62 (F1\({}^{2}\) = 0.70), ferritin values between 376.2 \(\upmu\)g/L and 396.0 \(\upmu\)g/L (F1\({}^{2}\) = 0.91) and PCT values between 0.2 \(\upmu\)g/L and 5.2 \(\upmu\)g/L (F1\({}^{2}\) = 0.95) (Figure 8 and Table 10). It can be said that the determined value ranges of these features are the most important lethal-risk levels. It is noteworthy that procalcitonin and ferritin are the most important feature pairs in the detection of surviving and non-surviving COVID-19 patients, according to both the HGB classifier and the two-threshold approach. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Feature Pairs**} & \multicolumn{4}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F1\({}^{2}\)**} \\ \hline  & & & **Surv. ** & **Non-Surv. ** & **Surv. ** & **Non-Surv. ** & **Surv. ** & **Non-Surv.