Cardiovascular disease is one of the leading causes of death worldwide. Many people develop heart problems without any early warning signs. Because of this, the risk analysis is very important in healthcare if it is correct and early. Traditional risk scores are often simple and incorporate a small number of factors. These methods may not be effective with all patients and populations. Machine learning has become a useful tool for cardiovascular risk analysis. It can research massive amounts of data regarding health and discover concealed patterns. Machine learning models can use data from electronic health records, medical images, ECG signals and wearable devices. Such models could be useful in predicting the risk of heart disease more accurately at an earlier stage and help doctors more accurately predict the risk of heart disease.This review paper is dedicated to study the recent work on machine learning methods for cardiovascular risk analysis. It discusses common sources of data, preprocessing techniques, and model types. Traditional machine learning models and deep learning methods are both discussed. The paper also describes how these models are applied in clinical decision support systems to assist doctors in making better decisions. In addition, this review raises important issues such as model validation, explainability, fairness and trust. Many challenges remain, however, including data quality issues as well as lack of real-world testing. Future research directions are also discussed, including explainable AI and privacy-preserving learning. Overall, this review demonstrates that machine learning has the potential to enhance cardiovascular risk analysis and aid in clinical decision-making. However, careful designing and clinical validation is required before widespread adoption.
