  Accuracies of survival models for life expectancy prediction as well as
critical-care applications are significantly compromised due to the sparsity of
samples and extreme imbalance between the survival (usually, the majority) and
mortality class sizes. While a recent random survival forest (RSF) model
overcomes the limitations of the proportional hazard assumption, an imbalance
in the data results in an underestimation (overestimation) of the hazard of the
mortality (survival) classes. A balanced random survival forests (BRSF) model,
based on training the RSF model with data generated from a synthetic minority
sampling scheme is presented to address this gap. Theoretical results on the
effect of balancing on prediction accuracies in BRSF are reported. Benchmarking
studies were conducted using five datasets with different levels of class
imbalance from public repositories and an imbalanced dataset of 267 acute
cardiac patients, collected at the Heart, Artery, and Vein Center of Fresno,
CA. Investigations suggest that BRSF provides an improved discriminatory
strength between the survival and the mortality classes. It outperformed both
optimized Cox (without and with balancing) and RSF with an average reduction of
55\% in the prediction error over the next best alternative.
