## 4 Discussion
Additionally, it used synthetic training data, which could pose a problem in real-world scenarios where the variability and complexity of actual patient data might not be fully captured by synthetic datasets. Similarly, [PERSON] et al. (2021) [11] estimated aPWV using waveform decomposition of central aortic pressure waveforms derived from radial pressure waveforms. This method achieved a relatively high \(R^{2}\) of 0.88 and an RMSE of 0.63 m/s using a 5-fold cross-validation. However, it relies on advanced signal processing techniques, which can introduce complexity and potential sources of error, especially under conditions with noisy or imperfect signals. Furthermore, the complexity of the analyses conducted in other studies is notable, as they all employed signal processing techniques. For example, [PERSON] et al. used tonomet \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Author** & \(R^{2}\) & **RMSE (m/s)** & **Validation** & **Metric** & **Type of Analysis** \\ \hline [PERSON] [PERSON] et al. (2023) [13] & 0.90 & 0.71 & 5-fold CV & cfPWV & PPG or Pressure Wave Signals \\ [PERSON], [PERSON] et al. (2021) [5] & 0.49 & 1.59 & Hold-out & cfPWV & Radial Pressure Wave Signals \\ [PERSON], [PERSON] et al. (2019) [12] & 0.94 & – & – & aPWV & PPG Signals \\ [PERSON], [PERSON] et al. (2018) [6] & 0.85 & 1.12 & Hold-out & cfPWV & Carotid Waveform \\ [PERSON], [PERSON] et al. (2021) [11] & 0.88 & 0.63 & 5-fold CV & aPWV & Waveform Decomposition \\ This work & 0.95 & 0.58 & External & aPWV & Clinical variables \\ \hline \hline \end{tabular} Abbreviations: \(R^{2}\) = Coefficient of Determination; RMSE = Root Mean Squared Error; CV = cross-validation; cfPWV = carotid– femoral pulse wave velocity; aPWV = aortic pulse wave velocity; PPG = photoplethysmography.  
\end{table} 
Table 5: Comparison of current work with existing studies on pulse wave velocity estimation. ric signals, while other studies utilized PPG (photoplethysmography) signals. Although these physiological signals have been proven effective for PWV prediction, they can contain noise, and their analysis or validation may require expert intervention. This complexity can introduce additional variability and potential sources of error in the prediction models. In contrast, the approach used in this study relies on clinical variables, which are typically more stable and easier to interpret. This difference underscores the potential advantage of this approach as it reduces the dependency on high-quality signal acquisition and expert analysis, making the model more accessible and potentially more reliable in diverse clinical settings. In this context, the analysis identified age, SBP, DBP, WC, weight, and height as the six most influential variables. Age and blood pressure are well-documented predictors of vascular health, reflecting their pivotal role in cardiovascular dynamics [24]. Indeed, it has been reported that AS and blood pressure increase with age and are independent predictors of cardiovascular events [25]. On the other hand, WC and weight provide crucial insights into body composition, an important factor in vascular stiffness and overall cardiovascular risk [26]. WC provides crucial insights into body fat distribution, which is a known factor influencing vascular stiffness and overall cardiovascular risk [27]. Height, while less directly related to arterial stiffness, plays an important role when considered alongside weight in the calculation of BMI. To this respect, BMI has been shown to be a predictor of AS syndrome, as studies indicate that increasing BMI is positively associated with vascular aging and arterial rigidity [28]. Therefore, height acts as a normalizing factor in cardiovascular assessments, ensuring that body proportions are accurately accounted for when evaluating cardiovascular risk. This normalization is essential for the accurate interpretation of anthropometric data in relation to arterial health, reinforcing the inclusion of height as an important variable in our predictive model.