Survival Analysis is a potent method for analyzing time-to-event data, particularly longitudinal follow-up studies with real-time or other definite methods for identifying discrete outcomes, wherein it makes full use of data regarding time and event of interest, to estimate the risk of outcome over time. Through this paper we use this advanced statistical modeling method, of Survival Analysis, specifically Cox Proportional Hazards Model and Kaplan Meier Curves to visualize, model and predict the next Cardiac Incident in a host of at-risk patients in the study conducted in the early 1990’s dubbed as the Long Term ST database, hosted by Physionet, an open access repository. The Long-Term ST database is a comprehensive collection of ECG signals collected as a Multinational Effort.
