# Identification of Electrocardiographic Patterns Related to Mortality with COVID-19
## 3 Results  
The AdvRS&LP produced 5 NNs (one for each cross-validation fold), whose architectures and performance are listed in Table 4. Despite the different architectures, all the NNs presented high-level performances, comparable across folds, in the hold-out testing sets (AUC > 80% and ACC > 79%). The best NN, composed of 714 neurons distributed in three layers, was related to the second fold: the highest AUC on the validation dataset (AUC = 86.42% and ACC = 83.17%). The worst performance was obtained in the 4 th fold (AUC = 80.89% and ACC = 79.21%). Table 5 shows the first 10 features of the ranking obtained for the univariate AUC and FR computed for each NN created by AdvRS&LP, while Figure 3 depicts the scatter plots of the comparisons between the AUC and FR for each NN and between the FRs of the different NNs. The FRs obtained by all the folds presented a poor correlation with the univariate AUC (\(\rho\) < 0.30, \(p\) < 0.05), but the agreement between the FRs obtained in the different folds was statistically strong (\(\rho\) > 0.97, \(p\) < 0.05). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Architecture**} & \multicolumn{2}{c}{**AUC (\%)**} & \multicolumn{2}{c}{**ACC (\%)**} \\ \cline{2-6}  & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Distribution of**} & \multicolumn{1}{c}{**Hold-Out Testing**} & \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**Hold-Out Testing**} \\  & \multicolumn{1}{c}{**Layer**} & \multicolumn{1}{c}{**Neurons**} & \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**Fold**} & \multicolumn{1}{c}{**Fold**} \\ \hline
**fold 1** & 3 & [101 100 555] & 99.11 & 86.05 & 99.01 & 79.21 \\ \hline
**fold 2** & 3 & [101 100 513] & 95.75 & 86.42 & 97.28 & 83.17 \\ \hline
**fold 3** & 1 & [736] & 94.61 & 86.00 & 97.24 & 81.19 \\ \hline
**fold 4** & 3 & [196 188 245] & 100.00 & 80.89 & 100.00 & 79.21 \\ \hline
**fold 5** & 4 & [101 100 100 228] & 97.41 & 82.18 & 98.51 & 79.21 \\ \hline
**Overall** & - & - & 97.38 \(\pm\) 2.25 & 84.31 \(\pm\) 2.58 & 98.42 \(\pm\) 1.17 & 80.40 \(\pm\) 1.77 \\ \hline \hline \end{tabular}  
ACC: accuracy; AUC: area under the curve of the receiver operating characteristic curve. Overall performance is reported in terms of average \(\pm\) standard deviation.  
\end{table} 
Table 4: Architecture and performance of the neural networks created by the Advanced Repeated Structuring and Learning Procedure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ranking**} & \multirow{2}{*}{**AUC (\%)**} & \multicolumn{4}{c}{**FR**} \\ \cline{2-6}  & & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** \\ \hline \multirow{2}{*}{1} & TpTeS on lead V6 & mnNN & mnNN & mnNN & mnNN & mnNN \\  & (0.65) & (0.78) & (0.78) & (0.78) & (0.78) & (0.78) \\ \hline \multirow{2}{*}{2} & PR on lead aVL & PT on lead V2 & QT on lead V6 & PT on lead II & PT on lead V6 & QT on lead aVR \\  & (0.65) & (0.78) & (0.77) & (0.71) & (0.74) & (0.75) \\ \hline \multirow{2}{*}{3} & SDNN & PT on lead aVR & [PERSON] on lead aVR & PT on lead aVR & PT on lead V4 & PT on lead aVR \\  & (0.64) & (0.78) & (0.77) & (0.72) & (0.77) & (0.76) \\ \hline \multirow{2}{*}{4} & PTD & PT on lead II & QT on lead aVF & [PERSON] on lead aVR & PT on lead V5 & PT on lead I \\  & (0.64) & (0.77) & (0.77) & (0.77) & (0.74) & (0.75) \\ \hline \multirow{2}{*}{5} & PS on lead aVL & PT on lead I & QT on lead II & PT on lead V4 & PT on lead II & QT on lead II \\  & (0.64) & (0.77) & (0.76) & (0.75) & (0.78) & (0.75) \\ \hline \multirow{2}{*}{6} & QA on lead II & PT on lead V3 & QT on lead V4 & PT on lead V1 & PT on lead aVR & QT on lead V6 \\  & (0.63) & (0.76) & (0.76) & (0.71) & (0.78) & (0.75) \\ \hline \multirow{2}{*}{7} & TpTeS on lead V5 & PT on lead VI & QT on lead V5 & QT on lead II & QT on lead aVR & PT on lead V6 \\  & (0.63) & (0.76) & (0.76) & (0.76) & (0.77) & (0.78) \\ \hline \hline \end{tabular}
\end{table}