Machine learning (ML) and large-scale big data are key factors in developing an accurate
prediction model for cardiovascular disease (CVD). Although the CVD risk often depends on …

Background: This study proposes a cardiovascular diseases (CVD) prediction model using
machine learning (ML) algorithms based on the National Health Insurance Service-Health …

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine
learning has attracted attention in analyzing increasingly large, complex healthcare data …

Background Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of
preventative cardiology. Risk prediction models currently recommended by clinical …

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable
information for conducting epidemiological studies. With the right tools, we can gain insights …

Background Cardiovascular diseases (CVD) are currently the leading cause of premature
death worldwide. Model-based early detection of high-risk populations for CVD is the key to …

Objectives Cardiovascular predictions are related to patients' quality of life and health.
Therefore, a risk prediction model for cardiovascular conditions is needed. Methods In this …

Objective To evaluate the performance of machine learning (ML) algorithms and to compare
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …

Background Current approaches to predict cardiovascular risk fail to identify many people
who would benefit from preventive treatment, while others receive unnecessary intervention …

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular
disease prediction. We aim to assess and summarize the overall predictive ability of ML …