## 3 Results
### Investigation of the Effectiveness of the Models Operating on the One-Feature HGB Model  
It is known that the use of the F1 score may be more useful than the accuracy value in cases where the data distributions are not equal. Figure 4 shows a comparison of F1 metrics for the survived-COVID-19 class, calculated for the original and SMOTE-balanced datasets. It can be seen that, for survived-COVID-19, there is a high F1 value for all features, for both databases. \begin{table}
\begin{tabular}{c l c} \hline \hline
**No** & **ML Models** & **F12** \\ \hline
1 & Histogram-based Gradient Boosting (HGB) & 1.0000 \\
2 & Adaboost (AB) & 0.9952 \\
3 & Extra Trees (ET) & 0.9952 \\
4 & K-nearest neighbors (KNN) & 0.9929 \\
5 & Random Forest (RF) & 0.9928 \\
6 & Support Vector Machine with Linear Kernel (SVM-LK) & 0.9904 \\
7 & Multimodal Naive Bayes (MNB) & 0.9881 \\
8 & Gaussian Naive Bayes (GNB) & 0.9646 \\
9 & Stochastic Gradient Descent (SGD) & 0.9642 \\
10 & Decision Tree (DT) & 0.9642 \\
11 & Bernoulli Naive Bayes (BNB) & 0.9563 \\
12 & Linear discriminant analysis (LDA) & 0.9431 \\
13 & Support Vector Machine with non-linear Kernel (SVM-NLK) & 0.9428 \\
14 & Multilayer Perceptron (MP) & 0.9011 \\
15 & Passive-Aggressive (PA) & 0.8772 \\
16 & Quadratic Discriminant Analysis (QDA) & 0.7212 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performance of ML models run with 34 features to detect patient groups. Figure 3: Performance of ML models in classifying surviving and non-surviving COVID-19 patients, using the 34 features. Figure 5 shows a comparison of F1 metrics for the non-survived-COVID-19 class calculated for the original and SMOTE-balanced datasets. It can be seen that, for non-survived-COVID-19, a high F1 value is observed for most of the features for SMOTE-balanced dataset, while for the original dataset, only some of the features have a high F1. Thus, to select the main features, it is logical to use the results of calculating the metrics for the original dataset. Synthetic data, although well approximated by the model, nevertheless does not allow us to judge the performance of the model with real data. Table A1 in the Appendix A presents the classification result of SARS-CoV-2-RBV3 dataset for the HGB model, using a single input-feature for original dataset, indicating the main classification metrics (Precision, Recall, F1). Figure 4: F1 metrics for survived-COVID-19 class, calculated for original and SMOTE-balanced datasets. Figure 5: F1 metric for non-survived-COVID-19 class, calculated for original and SMOTE-balanced datasets. F1\({}^{2}\) Metric in the Detection of Patient Groups with the HGB Model, One-Threshold, and Two-Threshold Approaches  
Figure 6 shows the dependence of F1\({}^{2}\) on the feature. Let us agree to consider as the most significant features those features in which F1\({}^{2}\)\(\geq\) 0.5; this threshold is visualized in the figure by the blue line. As a result, we obtain a list of the 12 most significant single features for HGB classification, shown in Table 5, in which F1\({}^{2}\)\(\geq\) 0.5. No high F1\({}^{2}\) value was found in the classification of patient groups with the one-threshold-value approach. However, high F1\({}^{2}\) values were found in the classification of patient groups with the HGB model operated with a single feature and the two-threshold-value approach. Accordingly, PCT and ferritin properties were found to be the most effective in classification, according to the HGB model operated with one feature and the two-threshold approach.