## 1 Introduction  
The World Health Organization (WHO) has reported that approximately 17.9 million individuals succumb to CardioVascular Disease (CVD) annually, establishing CVDs as a leading cause of global mortality [1]. Despite a decline in CVD mortality rates over the last three decades, this favorable trend is beginning to plateau, raising concerns about a potential reversal unless substantial and concerted efforts are undertaken. This challenge necessitates a transformative stance towards preventive healthcare, whereby our response transcends mere reaction to illness and instead emphasizes the anticipation and interception of risk factors before they converge into potentially devastating outcomes. Accurately predicting CVD risk based on personal lifestyle factors is pivotal for facilitating early intervention and implementing preventive measures. Nevertheless, diagnosis poses a significant challenge for practitioners due to the intricate nature of CVDs, which are frequently conflated with signs of aging. In this context, machine learning (ML) algorithms have emerged as invaluable assets, leveraging their capacity to uncover intricate patterns and relationships within datasets, empowering healthcare practitioners with predictive tools for the early detection and intervention of CVD [2]. CVDs refer to a group of conditions that affect the heart and blood vessels, often leading to complications such as heart attacks, strokes, and other circulatory disorders [3]. These conditions can arise due to factors such as plaque buildup in the arteries (atherosclerosis), hypertension (high blood pressure), inflammation, and various lifestyle factors such as diet, physical activity, and smoking [3; 4]. Symptoms of CVD can range from chest pain and shortness of breath to numbness and weakness in the extremities, highlighting the diverse nature of these conditions [5; 6]. In this context, prevention emerges as the cornerstone for maintaining good health and detecting potential health concerns at an early stage before they escalate into complications or become more challenging to manage [7]. Regrettably, the uptake of preventive measures falls significantly short of what is necessary. A study from 2018 revealed that merely 8% of adults in the United States aged 35 years and above received the recommended preventive care [8]. This disparity underscores the substantial gap between the potential advantages of preventive healthcare and its current implementation. Consequently, the role of technology, data analytics, and predictive modeling in preventive healthcare has become increasingly vital. The advancements in technology facilitate a health management approach that is not only more vigilant but also highly personalized. Through the application of data analytics and predictive models, healthcare professionals can meticulously analyze extensive datasets comprising crucial information, ranging from an individual's medical history and lifestyle choices to genetic predispositions and beyond [9]. This thorough analysis enables the identification of intricate patterns and risk factors, particularly those associated with conditions like CVD. The utility of these advanced tools transcends mere pattern recognition. It enables the precise identification of high-risk individuals before clinical symptoms manifest, thus expediting the deployment of targeted interventions and tailored treatment strategies [8; 10]. Early detection holds transformative potential in disease management and patient outcomes. By identifying the onset of health issues at their nascent stages, healthcare providers can initiate timely and personalized interventions tailored to individual needs. This enhances treatment effectiveness, reduces complications, alleviates the burden on healthcare resources, and ultimately improves overall patient quality of life [11]. Consequently, early detection not only yields medical benefits but also optimizes the overall healthcare system and promotes population health resilience.