New variations of random survival forests and applications to age-related disease data

This work addresses a type of survival prediction (or survival analysis) problem, where the goal is to predict the time passed until an individual is diagnosed with a certain age-related disease. Survival prediction is more challenging than standard regression because the former involves censored data, i.e. individuals who have not been diagnosed with the disease yet. Random Survival Forests (RSFs) are a powerful type of Random Forest algorithm developed specifically for survival analysis. In this work we investigate new variations of RSFs, namely variations in the node-splitting criterion and the leaf-node-prediction criterion. Results of experiments on 10 real-world survival prediction problems show that, although the variations in node-splitting criteria did not lead to significant differences in predictive performance, RSFs with a new proposed leaf-node-prediction criterion had significantly better predictive performance than standard RSFs.