Some variations on Random Survival Forest with application to Cancer Research

Random survival forest can be extremely time consuming for large data set. In this paper we propose few computationally efficient algorithms in prediction of survival function. We explore the behavior of the algorithms for different cancer data sets. Our construction includes right censoring data too. We have also applied the same for competing risk survival function.