Improving the early prediction of cardiovascular diseases by developing novel data-driven machine learning and multiomics approaches

Cardiovascular disease (CVD) begins decades before the clinical manifestations and silently reaches to irreversible advanced and serious stage. Therefore, primary prevention is of paramount importance in controlling the disease and related health care costs. Still, most existing risk prediction tools are developed for clinical cardiovascular outcomes that have limited value for primary prevention. The main aim of this study is to develop robust early cardiovascular risk prediction tool using multi-omics predictors identified with a novel machine learning method using longitudinal, multigenerational and multicohort datasets. This research project has potential to rationally shift the paradigm in preventive cardiology from detecting the likelihood of developing the disease to detecting the early biomarkers in otherwise asymptotic individuals and focus on people's lifestyle for improving cardiovascular health status.