## 4 Discussion  
This study aimed to estimate an individual's VAAG using SL models based on frequency features obtained from the BAPW. Our findings highlighted the potential of said features as a compelling alternative to temporal and amplitude features, which often require higher sampling frequencies and sophisticated detection algorithms. To begin, one of the main contributions of this study was that frequency features revealed age-dependant variations, displaying either an increase or decrease with advancing age. For instance, the HD feature manifested an augmentation trend with age, while the \(C_{n}\) feature of the fourth harmonic demonstrated a decline. Other features, which appear unaltered by age, suggested that employing dimensionality reduction algorithms could potentially enhance the performance of the SL models applied. Additionally, an integrated dataset encompassing both simulated and real-world subjects was proposed, resulting in significant improvements in model accuracy. Importantly, the use of subjects with risk factors (belonging to a local clinical dataset) for testing purposes contributed to the evaluation of model performance, where differences in VAAG could be expected. This underscores the critical importance of incorporating real-world subject data and considering risk factors to enhance the precision of VAAG estimates, ultimately contributing to more effective preventive strategies and improved cardiovascular health outcomes. The use of simulated models enables the training of SL models on a larger population and is not dependent on the number of volunteers who can participate. Different health statuses could be simulated, allowing the inclusion of not only healthy subjects but also pathological ones. However, it is worth noting that hemodynamic theoretical values do not always align with those of real subjects, emphasizing the importance of real subject data. A significant variability in these variables from different subjects on similar conditions has been observed [24; 25]. In this sense, it is essential to strike a balance between simulation and real-world data, ensuring the models remain grounded in reality. This approach will contribute to more robust and insightful results in the field of healthcare research. Our findings are in agreement with those of [PERSON] et al. [16], as both studies focus on VA estimation using similar frequency features and SL. While [PERSON] et al. [16] aimed to discriminate between \"Vascular Aging Group\" and \"Control Group\" using binary classification with the Cardio-ankle vascular index (CAVI) as a guide of each subject's arterial stiffness status, the present study pursued a differentiated classification methodology. A multi-class classification was implemented to estimate VAAG within six different groups, serving as a surrogate of CHA. On the contrary, in this study, multiple SL models were trained with (simulated and real) healthy subjects and subsequently tested with both pathologi  
Figure 18: Distribution of BMI in the Local Clinical In Vivo dataset. cal and non-pathological subjects to detect VA abnormalities. In contrast, [PERSON] et al. [16] utilized both healthy and pathological individuals for training. To the best of our knowledge, no other studies have utilized spectrum features for the estimation of VA or other AS indicators. The findings of Case Study 1 provided valuable insights that justify the need for Case Study 2. The metrics obtained from Case Study 1 demonstrated excellent performance when applied to the in silico subjects. However, when tested on real-world subjects, the results deviated from the expected outcomes. Notably, a significant bias was observed towards the 25- and 75-year-old VAAGs, pointing out a trend to predict extreme values if the subject fell outside the theoretical limits for all features. In contrast, Case Study 2 yielded improved results.