  Cardiovascular disease remains a leading global cause of mortality,
necessitating accurate risk prediction tools. Traditional methods, such as
QRISK and the Framingham heart score, exhibit limitations in their ability to
incorporate comprehensive patient data, potentially resulting in incomplete
risk factor consideration. To address these shortcomings, this study conducts a
meticulous review focusing on the application of machine learning models to
enhance predictive accuracy. Machine learning models, such as support vector
machines, and Random Forest, as well as deep learning techniques like
convolutional neural networks and recurrent neural networks, have emerged as
promising alternatives. These models offer superior performance, accommodating
a broader spectrum of variables and providing precise subgroup-specific
predictions. While machine learning integration holds promise for enhancing
risk assessment, it presents challenges such as data requirements and
computational constraints. Additionally, large language models have
revolutionised healthcare applications, augmenting diagnostic precision and
patient care. This study examines the core aspects of cardiovascular disease
event risk and presents a thorough review of traditional and machine learning
models, alongside deep learning techniques, for improved accuracy. It offers a
comprehensive survey of relevant datasets, critically compares ML models with
conventional approaches, and synthesizes key findings, highlighting their
implications for clinical practice. Furthermore, the potential of machine
learning and large language models in cardiovascular medicine is undeniable.
However, rigorous validation and optimisation are imperative before widespread
application in healthcare. This integration promises more accurate and
personalised cardiovascular care.
