Some variations on Ensembled Random Survival Forest with application to Cancer Research

TL;DR

This paper introduces a novel AdaBoost-based ensemble method for survival prediction, incorporating right censoring and competing risks, and compares various algorithm variations on multiple datasets.

Contribution

It presents a new AdaBoost implementation for survival analysis that handles complex data types and evaluates its performance against different algorithm variations.

Findings

The proposed method effectively predicts survival functions.

Algorithm variations show different trade-offs in accuracy and run time.

The approach is validated on multiple real-world datasets.

Abstract

In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction includes right censoring data and competing risk data too. We take different data set to illustrate the performance of the algorithms.