## 4 Discussion
However, a bias towards the 25-year-old VAAG was still evident due to the imbalance given by the age distribution of the in vivo dataset (Figure 1) that revealed a predominantly young population, possibly explaining the bias towards the group with a greater number of datapoints. Furthermore, the local clinical in vivo dataset, which mainly comprises pathological subjects, was also tested against Case Study 2 SL models. As was previously mentioned, the metrics were expected to be low due to the fact that the estimated VAAG presented higher values than CHA. Notably, Table 9 demonstrates that the majority of subjects were indeed estimated to have a higher VAAG, aligning with the primary objective of the study. The comparative analysis of SL models, encompassing RF, SVM and MLP, unveiled the superior performance of the RF model, evidenced by its highest accuracy and overall metrics when contrasted with SVM and MLP. This trend is consistent across the two distinct case studies. In Case Study 1 targeting the in silico population, models exhibited metrics closely aligned with exceptional results, as demonstrated by AUC scores approaching unity. However, evaluating the RF model against in vivo measurements revealed limitations due to inherent biases, restricting full class-based evaluation and causing relatively lower metrics, a challenge potentially shared by other algorithms. The ROC curves for the in vivo dataset exhibited complexity. In Case Study 2, the RF model's superiority strengthened, showing a wider performance gap compared to Case Study 1. Robust performance was witnessed in the in silico dataset, while in the in vivo dataset, inherent bias impacted metric variations across classes. Notably, ROC curves, especially for the in silico dataset, maintained a near-perfect trend, while Case Study 2 curves consistently favored the left side of the central function. When scrutinizing the RF model from Case Study 2 against a local clinical in vivo dataset, AUC scores ranged from 0.87 (highest) to 0.50 (lowest), indicating favorable performance diversity. Overall, these findings underline the RF model's prowess in addressing bias-related challenges and delivering robust performance across diverse datasets, both simulated and real-world. The local clinical in vivo dataset was composed by subjects that exhibited one or more cardiovascular risk factors, evidencing that VA could be altered [26]. This observation supports the rationale behind the higher predicted VAAG for the study participants. Notably, a subset of subjects solely presented family history as a risk factor, which could not be considered as a risk group. This subgroup, under normal circumstances, might be overlooked, as they may not present symptoms or conditions. However, given that they could harbor underlying issues that may manifest in 5 to 10 years, this subgroup emerges as a prime candidate for a preventive medicine approach. Targeting this population could potentially yield significant benefits in terms of improving cardiovascular health and overall well-being while addressing potential future health challenges. A critical challenge in the presented methodology arose from bias, particularly from the in vivo dataset, given its abundance of younger individuals. This imbalance proved to influence the training process, and, in consequence, the results lead to inaccuracies in the predictions. This is acknowledged as a limitation of our study. To address this issue, data augmentation techniques could be employed. Through data augmentation, synthetic BAPW could be generated within underrepresented VAAG, thereby achieving a more balanced dataset. This strategy is designed to enhance the robustness of SL models and improve their ability to generalize, ensuring more reliable predictions across all VAAG. Further data collection would be needed to support data augmentation from several subjects. For a thorough validation of the proposed approach, it is imperative to have a dataset comprising both control subjects and those with known pathologies.