women [10], and individuals with atypical ECG markers such as fractionated QRS [11]. Several studies have also underscored the link between autonomic nervous system imbalance and cardiovascular risk through reduced heart rate variability (HRV), pointing to its potential as an early physiological marker [12; 13; 14]. It has been well established that factors such as smoking, hypertension, diabetes, and dyslipidemia contribute significantly to the development and progression of cardiovascular disease [15; 16; 17; 18; 19; 20; 21]. For instance, disorders of pregnancy have been associated with increased long-term cardiovascular risk in women [15], and social determinants like isolation or lack of support are now recognized as non-traditional yet potent contributors to poor cardiovascular outcomes [16]. Moreover, sex-specific differences in clinical presentation and biomarker profiles demand greater attention in both research and practice [17]. Lifestyle factors such as poor sleep and nutrition are also gaining recognition as modifiable risk factors that influence vascular health and autonomic regulation [18; 20], while large-scale cohort studies such as the CARLA Study have begun integrating HRV metrics into routine cardiovascular risk assessments in the elderly population [19]. These findings reinforce the importance of a multifaceted approach to identifying at-risk individuals. Furthermore, a study by [PERSON] et al. (2021) investigated the redundancy between different cardiovascular risk indicators derived from heart rate variability (HRV) and heart rate dynamics, using big data analytics from the ALLSTAR project [22]. Their findings highlighted that while multiple metrics may individually show predictive value, substantial redundancy exists among them, implying overlapping physiological information. This underscores the need for selecting non-redundant, orthogonal predictors in risk modeling to avoid inflated predictive performance due to collinearity. Such insights are critical as the field moves toward integrating high-dimensional physiological data into machine-learning frameworks for CVD prediction. Building on this, research is also progressing on heart rate-based indicators as valuable tools for cardiovascular risk stratification [23; 24; 25; 26; 27]. For instance, low HRV has been associated with increased post-myocardial infarction mortality, especially in the presence of depressive symptoms [23]. Randomized controlled trials have shown that structured interventions such as exercise and stress management can significantly improve cardiovascular risk profiles through autonomic modulation [24]. Moreover, complex heart rate dynamics--such as non-Gaussian distributions or fractal loss--have emerged as independent prognostic markers in patients with heart failure or depression [25; 26]. More recently, machine-learning-based approaches utilizing cohort data like the Suita Study have demonstrated how advanced algorithms can integrate traditional and physiological predictors, including HRV, to enhance coronary heart disease prediction [27]. These studies point to the growing relevance of combining physiological signal processing with modern data science in cardiovascular risk assessment. However, these previous studies primarily identified risk factors based on clinical measurements obtained during hospital visits, rather than data collected in everyday life. As a result, the practical application of these findings for continuous and real-time cardiovascular risk monitoring in daily settings has not yet been fully realized. There remains a need to explore how cardiovascular risk can be assessed more dynamically and flexibly outside traditional clinical environments. The Framingham Heart Study (FHS) provides a valuable and historically significant resource for investigating cardiovascular disease (CVD) risk factors.