Event prediction has been the cornerstone of cardiovascular epidemiology and have allowed us to characterize sub-clinical disease processes and target key risk factors for modification. Epidemiological studies used to derive such predictive models frequently contain hundreds of variables from multiple tests. Random survival forests may be an effective machine learning strategy for incident event prediction in large populations with large phenotypic datasets. These methods do not require a priori assumptions regarding causality and may thus be suitable to defining the role of novel biomarkers and tests (such as imaging, biomarker panels, ECG, etc) for cardiovascular disease prediction. We explore the role of MRI in the prediction of incident heart failure and all-cause death.