Accuracies of survival models for life expectancy prediction as well as lifesaving critical-care applications are significantly compromised due to the sparsity of samples and extreme imbalance between the survival and mortality classes in addition to the invalidity of the popular proportional hazard assumption. An imbalance in data results in an underestimation (overestimation) of the hazard of the mortality (survival) classes. Balanced random survival forests (BRSF) model, based on training random survival forests with balanced data generated from a synthetic minority sampling scheme is presented to address this gap. Theoretical findings on the improvement of survival prediction after balancing are corroborated using extensive empirical evaluations. Benchmarking studies consider five data sets of different levels of class imbalance from public repositories and an imbalanced survival data set of 267 ST-elevated myocardial infarction (STEMI) patients collected over a period of one year at Heart, Artery, and Vein Center of Fresno, CA. Investigations suggest BRSF provides a better discriminatory strength between the censored and the mortality classes and improves survival prediction of the minority. BRSF outperformed both optimized Cox (without and with balancing) and RSF with a 55% reduction (averaged over all 6 data sets) in prediction error over the next best alternative.
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