Kernel Induced Random Survival Forests (KIRSF) is a statistical learning
algorithm which aims to improve prediction accuracy for survival data. As in
Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each
individual in the test set. Prediction error is estimated using Harrell's
concordance index (C index) [Harrell et al. (1982)]. The C-index can be
interpreted as a misclassification probability and does not depend on a single
fixed time for evaluation. The C-index also specifically accounts for
censoring. By utilizing kernel functions, KIRSF achieves better results than
RSF in many situations. In this report, we show how to incorporate kernel
functions into RSF. We test the performance of KIRSF and compare our method to
RSF. We find that the KIRSF's performance is better than RSF in many occasions.