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

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 …

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 …

Background Cardiovascular disease (CVD) risk prediction is important for guiding the
intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use …

Cardiovascular disease (CVD) makes our heart and blood vessels dysfunctional and often
leads to death or physical paralysis. Therefore, early and automatic detection of CVD can …

Aims To analyse the predictive capacity of 15 machine learning methods for estimating
cardiovascular risk in a cohort and to compare them with other risk scales. Methods We …

Purpose This review aims to summarize and evaluate the most accurate machine-learning
algorithm used to predict ischemic heart disease. Methods This systematic review was …

Rationale: Machine learning may be useful to characterize cardiovascular risk, predict
outcomes, and identify biomarkers in population studies. Objective: To test the ability of …

The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD)
risk in patients with characteristics within prespecified ranges and has uncertain …