## 3 Results
### Case Study 1: In Silico Dataset  
The study population in Case Study 1 consisted entirely of in silico subjects, a cohort of 4374 simulated individuals. Evaluation metrics for Case Study 1 are presented in Table 2. The RF model presented the best accuracy and best performance for the test dataset. However, SVM and MLP showed competitive results as well. Confusion Matrix and the ROC Curve for the best-performing model are shown in Figures 4 and 5.  
Subsequently, the RF model was evaluated against both in vivo datasets. Table 3 presents the obtained metrics, while Figures 6 and 7 show the Confusion Matrix and the ROC Curve for the in vivo dataset, and Figures 8 and 9 for the local clinical in vivo dataset for the best-performing model. The results from the in vivo dataset revealed a significant bias towards the age group extremes, that is, the 25- or 75-year-old age groups. Table 4 indicates the amount of subjects from both in vivo datasets that estimated an equal, greater of lower VAAG. The metrics for the local clinical in vivo dataset indicate a less-than-desirable performance, which could be expected given its composition of both healthy and pathological subjects. Nevertheless, it is worth noting a distinct trend towards the 25-year-old age group.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metrics (\%)**} & \multicolumn{4}{c}{**Models**} \\ \cline{2-5}  & **SVM** & **RF** & **MLP** \\ \hline Accuracy CV & & 94.45 & 98.03 & 95.03 \\ Accuracy Test & & 96.68 & 97.60 & 97.71 \\ \hline \multirow{5}{*}{F1-Score} & 25 & 97.31 & 95.44 & 98.64 \\  & 35 & 95.51 & 95.54 & 97.16 \\  & 45 & 95.20 & 97.52 & 96.55 \\  & 55 & 95.36 & 99.66 & 96.34 \\  & 65 & 98.11 & 98.47 & 97.72 \\  & 75 & 98.93 & 99.29 & 100 \\ \hline \multirow{5}{*}{Precision} & 25 & 96.02 & 91.82 & 98.64 \\  & 35 & 97.38 & 96.77 & 97.47 \\ \cline{1-1}  & 45 & 93.29 & 98.57 & 95.23 \\ \cline{1-1}  & 55 & 95.36 & 99.34 & 96.67 \\ \cline{1-1}  & 65 & 98.48 & 100 & 98.47 \\ \cline{1-1}  & 75 & 100 & 100 & 100 \\ \hline \multirow{5}{*}{Recall} & 25 & 98.64 & 99.32 & 98.64 \\ \cline{1-1}  & 35 & 93.71 & 94.33 & 96.85 \\ \cline{1-1}  & 45 & 97.20 & 96.50 & 97.90 \\ \cline{1-1}  & 55 & 95.36 & 100 & 96.02 \\ \cline{1-1}  & 65 & 97.74 & 96.99 & 96.99 \\ \cline{1-1}  & 75 & 97.88 & 98.59 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics for Case Study 1. Results from Training CV and In Silico Test Dataset.  
Figure 5: ROC curve for the In Silico Dataset of Case Study 1.  
Figure 6: Confusion Matrix for the In Vivo Dataset of Case Study 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}
**Metrics (\%)** & \multicolumn{2}{c}{**In Vivo**} & \multicolumn{2}{c}{**Local Clinical In Vivo**} \\ \hline Accuracy & & 60.74 & 15.62 \\ \hline \multirow{5}{*}{F1-Score} & 25 & 77.03 & 47.06 \\  & 35 & NDA & 12.5 \\  & 45 & NDA & NDA \\  & 55 & NDA & NDA \\  & 65 & NDA & NDA \\  & 75 & 19.84 & NDA \\ \hline \multirow{5}{*}{Precision} & 25 & 65.52 & 40 \\  & 35 & NDA & 14.28 \\  & 45 & NDA & NDA \\  & 55 & NDA & NDA \\  & 65 & NDA & NDA \\  & 75 & 13.54 & NDA \\ \hline \multirow{5}{*}{Recall} & 25 & 93.46 & 57.14 \\  & 35 & NDA & 11.11 \\ \cline{1-1}  & 45 & NDA & NDA \\ \cline{1-1}  & 55 & NDA & NDA \\ \cline{1-1}  & 65 & NDA & NDA \\ \cline{1-1}  & 75 & 37.14 & NDA \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics for Case Study 1. Results from In Vivo and Local Clinical In Vivo datasets.  
Figure 7: ROC curve for the In Vivo Dataset of Case Study 1.