## 4 Discussion  
The cross-validation results demonstrated a strong model fit for all the proposed models, with PR and NN emerging as the top performers. PR's superior performance can be attributed to its ability to explicitly model nonlinearities through polynomial terms, allowing it to capture subtle variations in AS more effectively. Similarly, NN may benefit from its hierarchical structure, which enables it to learn complex vascular aging patterns  
Figure 4: Scatter and residual plots for model performance on the VascuNET dataset. The **top row** displays the predicted vs. real aPWV values for each model, while the **bottom row** shows the residuals, with an expected distribution centered around zero. **Blue dots** represent individual data points. The **red dashed lines** indicate the identity line (**top row**) and zero-error line (**bottom row**), serving as references for ideal prediction and perfect residuals, respectively. Figure 5: Scatter and residual plots for model performance on the ExIC-FEp dataset. The **top row** displays the predicted vs. actual aPWV values for each model, while the **bottom row** shows the residuals, highlighting differences in prediction errors. **Blue dots** represent individual data points. The **red dashed lines** indicate the identity line (**top row**) and zero-error line (**bottom row**), serving as references for ideal prediction and perfect residuals, respectively. across multiple layers. These findings align with previous research emphasizing the advantages of nonlinear models in biomedical applications, particularly in the context of cardiovascular risk assessment [22; 23]. Interestingly, although SVR is typically effective in handling high-dimensional data, it did not achieve the same level of accuracy as PR and NN. One possible explanation is that SVR, particularly with the RBF kernel, optimizes a margin around the data, which may be less effective when the underlying relationships are nonlinear but smooth. This limitation became especially evident in the ExIC-FEp dataset, where SVR experienced a drastic drop in predictive performance (\(R^{2}=-0.21\)), failing to adapt to extreme AS values. In contrast, PR and NN maintained strong predictive capabilities (\(R^{2}>0.90\)), suggesting that models incorporating nonlinear transformations are better suited for predicting aPWV in both healthy and pathological populations. The relatively higher errors observed in SVR and boosting-based models indicate that, while these approaches may still generalize well, they likely require further kernel optimization or alternative feature engineering to enhance their adaptability to diverse patient profiles. Table 5 shows a comparison between the current work and existing studies on PWV estimation. It is important to note that while each study employs its specific methodology, and, therefore, the results should be interpreted with caution, the performance in terms of \(R^{2}\) adjustment is quite similar among them. Only the study by [PERSON], [PERSON] et al. [5] seems to report a significantly lower adjustment. To this respect, the methodology proposed in this work achieves the lower RMSE, with a score of 0.58 m/s. This result is particularly noteworthy because, unlike the other studies that use internal validation methods such as K-fold cross-validation or hold-out validation, this work employed two different datasets for external validation, which were not used in the training phase. Consequently, this approach provides a clearer indication of the predictive model's ability to generalize results, thus demonstrating its robustness and applicability to diverse patient populations. It is also worth noting that very few studies have attempted to predict PWV, whether in its cfPWV or aPWV form. In this regard, the most direct comparison to this work would be with the method proposed by [PERSON], [PERSON] et al. [12]. However, this study only reported the \(R^{2}\) and not the RMSE or validation scheme.