Abstract
The objective of this paper is to propose an efficient regression algorithm of survival analysis - SurvivalBoost.. This algorithm is based on Random Survival Forests (RSF) and XGBoost. By combining the Elastic-Net penalty type Cox proportional hazards regression model with XGBoost optimal algorithm, our algorithm is more suitable for survival analysis. The performance of the proposed algorithm is compared with the Cox proportional hazards regression model, XGBoost, CoxBoost, RSF and Gradient Boosting Desicion Tree-based survival regression model on 4 simulated datasets and 4 real survival datasets. The results illustrated the superiority of the proposed algorithm.
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