Random survival forests (RSF) are an extension of random forests for analyzing right-censored survival data. RSF uses survival trees with new splitting rules and a missing data algorithm for imputation. A conservation-of-events principle defines ensemble mortality, a simple measure of mortality. RSF was tested on various datasets, including a case study on body mass and coronary artery disease. RSF outperformed other methods in prediction accuracy, especially in handling complex survival data. RSF uses bootstrap samples and survival trees to estimate cumulative hazard functions (CHF). Ensemble mortality is derived from the average CHF across trees. RSF also includes a novel missing data algorithm for both training and testing data. The algorithm imputes missing values by drawing from in-bag data, ensuring unbiased error estimates. RSF was applied to a large dataset of patients undergoing coronary artery bypass grafting (CABG), revealing complex relationships between body mass index, renal function, and survival. The results showed that survival increases with body mass up to a certain point, then decreases, highlighting the importance of renal function. RSF provides accurate predictions and handles missing data effectively, making it a valuable tool for survival analysis.Random survival forests (RSF) are an extension of random forests for analyzing right-censored survival data. RSF uses survival trees with new splitting rules and a missing data algorithm for imputation. A conservation-of-events principle defines ensemble mortality, a simple measure of mortality. RSF was tested on various datasets, including a case study on body mass and coronary artery disease. RSF outperformed other methods in prediction accuracy, especially in handling complex survival data. RSF uses bootstrap samples and survival trees to estimate cumulative hazard functions (CHF). Ensemble mortality is derived from the average CHF across trees. RSF also includes a novel missing data algorithm for both training and testing data. The algorithm imputes missing values by drawing from in-bag data, ensuring unbiased error estimates. RSF was applied to a large dataset of patients undergoing coronary artery bypass grafting (CABG), revealing complex relationships between body mass index, renal function, and survival. The results showed that survival increases with body mass up to a certain point, then decreases, highlighting the importance of renal function. RSF provides accurate predictions and handles missing data effectively, making it a valuable tool for survival analysis.