## 4 Discussion  
This study identified glucose levels and blood pressure--particularly diastolic pressure--as major predictors of cardiovascular mortality, reaffirming findings from previous research. Elevated glucose levels are strongly associated with increased cardiovascular risk through mechanisms involving diabetes and metabolic syndrome, while persistently high diastolic blood pressure contributes to vascular stress and the development of arteriosclerosis and heart failure. These results underscore the importance of accurate and, ideally, continuous monitoring of these physiological parameters. Recent advances in minimally invasive glucose monitoring using interstitial fluid [43] and non-invasive blood pressure estimation via optical sensors [44; 45; 46; 47; 48; 49; 50] have the potential to enhance individual-levelrisk monitoring outside clinical settings. These technologies enable dynamic assessment of cardiovascular health and support early interventions.  
In this study, we directly compared the proposed machine-learning models with the classical Framingham Risk Score (FRS) using the Framingham Heart Study dataset. XGBoost achieved the highest AUC of 0.83, significantly outperforming FRS (AUC = 0.78) with a relative improvement of approximately 6.4%. This enhancement in discrimination indicates the potential clinical utility of machine-learning approaches in more accurately identifying individuals at high cardiovascular mortality risk, thereby enabling earlier and more targeted interventions.  
However, several limitations must be acknowledged. First, the Framingham dataset, while well established, reflects a relatively homogeneous population--primarily middle-class white individuals in Massachusetts--limiting its applicability to broader, more diverse populations. While our models showed high predictive performance within the Framingham cohort, generalizability to demographically diverse populations remains a critical challenge. Previous studies, such as those using the Multi-Ethnic Study of Atherosclerosis (MESA) and the Jackson Heart Study, have demonstrated that machine-learning models trained on homogeneous data may underperform in different ethnic or socioeconomic groups. Future validation on independent cohorts with diverse demographics is essential to assess model robustness and ensure equitable cardiovascular risk prediction across populations.  
Second, although sex-based differences in cardiovascular risk were not prominent in our reanalysis, gender bias remains an important consideration. Cardiovascular risk increases significantly in postmenopausal women due to hormonal changes--an aspect not fully captured in the Framingham dataset [51; 52; 53; 54; 55; 56]. Moreover, some prior studies have shown that women may experience a higher relative risk of cardiovascular events from diabetes and hypertension compared to men.  
Third, the analysis was limited to 15 parameters derived from the Framingham dataset, excluding potentially important factors such as inflammatory biomarkers, genetic predisposition, mental stress, and detailed lifestyle information (e.g., diet, physical activity, sleep). Integrating these factors--potentially via wearable biosensors--could substantially improve risk stratification and enable more personalized health management.  
In conclusion, this study contributes to the growing body of research on AI-driven cardiovascular risk prediction by demonstrating the superiority of machine-learning models over traditional scoring systems like FRS. These findings support the use of such models in early detection, targeted intervention, and preventive public health strategies. With further validation and integration of diverse data sources, machine-learning approaches hold promise for advancing personalized cardiovascular care.