## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.3 Comparisons Between the Combined Groups A and B and Group C
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Feature Selection Mode** & **Feature Subsets** & **AUC** & **Accuracy** & **Precision** & **Recall** & **Specificity** \\ \hline \multirow{8}{*}{**Dataset**} & Full dataset & All 9 laboratory biomarkers & 0.728 & 0.620 & 0.664 & 0.620 & 0.663 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils, creatinine, LDH, platelets, CRP, neutrophils & 0.740 & 0.620 & 0.664 & 0.620 & 0.663 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils, creatinine, LDH, platelets, CRP & 0.745 & 0.700 & 0720 & 0.700 & 0.717 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils, creatinine, LDH & 0.787 & 0.760 & 0.766 & 0.760 & 0.757 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils, creatinine, LDH & 0.837 & 0.800 & 0.814 & 0.800 & 0.817 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils, creatinine & 0.842 & 0.760 & 0.766 & 0.760 & 0.757 \\ \cline{2-8}  & & Lymphocytes, RBCs, eosinophils & 0.830 & 0.740 & 0.774 & 0.740 & 0.777 \\ \cline{2-8}  & & Lymphocytes, RBCs & **0.887** & **0.820** & **0.840** & **0.820** & **0.847** \\ \cline{2-8}  & & Lymphocytes & 0.743 & 0.660 & 0.663 & 0.660 & 0.640 \\ \cline{2-8}  & & Univariate & RBCs & 0.813 & 0.840 & 0.845 & 0.840 & 0.843 \\ \hline \multirow{8}{*}{**Dataset**} & Full dataset & All 34 variables & 0.820 & 0.750 & 0.857 & 0.750 & 0.875 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, IL-28, IL-1 NN, CCL5, IL-18, PARK7 & 0.825 & 0.680 & 0.786 & 0.680 & 0.770 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, IL-28, IL-1 NN, CCL5, IL-18 & 0.853 & 0.700 & 0.795 & 0.700 & 0.783 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, IL-28, IL-1 NN, CCL5 & 0.882 & 0.640 & 0.768 & 0.640 & 0.743 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, IL-28, IL-1 NN & **0.885** & 0.660 & 0.777 & 0.660 & 0.757 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, VEGF, IL-28 & 0.818 & 0.660 & 0.777 & 0.660 & 0.757 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF & 0.859 & 0.600 & 0.750 & 0.600 & 0.717 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs, IFN-\(\gamma\) & 0.871 & 0.700 & 0.795 & 0.700 & 0.783 \\ \cline{2-8}  & & Lymphocytes, CXCL9, RBCs & **0.875** & **0.780** & **0.833** & **0.780** & **0.837** \\ \cline{2-8}  & & Lymphocytes, CXCL9 & 0.787 & **0.780** & 0.814 & **0.780** & 0.820 \\ \cline{2-8}  & & Univariate & IFN-\(\beta\); IFN-\(\gamma\); IL-2R; VEGF, lymphocytes; RBCs & 0.823 & 0.680 & 0.673 & 0.680 & 0.770 \\ \hline \hline \end{tabular}  
* The highest results for each column regarding the model’s performances are highlighted in bold for each dataset.  
\end{table} 
Table 8: _Cont._again the value of hybrid models and their capacity to better reflect patients' conditions, even in heterogenous populations. Employing the best variables from the best Naive Bayes models in the t-SNE analysis led to notable improvements in the separation of data according to patient groups (Figure 6d-f). In these instances, the control Group (A + B) exhibited greater dispersion in the score plot, which could be attributed to its high heterogeneity, since it included both patients with and without IMV, in contrast to Group C that included only patients under IMV.