An Effective Adaptive Ensemble Survival Model for Risk Prediction

Yu-Hsuan Wu, Ching-Chih Lee, Cheng-Hsin Chuang, Chun-Wei Tsai

Abstract: Several machine learning based methods were presented to generate the survival model for predicting the hazard ratio of the covariates of patients in recent years. To enhance the risk prediction for survival analysis, an adaptive ensemble survival model (AESurv) is presented in this paper. The proposed method combines a set of well-known survival models—namely, Cox proportional hazards model, random survival forest, DeepSurv, DeepHit, neural multi-task logistic regression model, and CoxTime—as an "ensemble model" to improve the generalization ability for risk prediction. Also proposed in this paper are an "adaptive voting neural network" to optimize the prediction results of the proposed ensemble model for different datasets and a "hyperparameter optimization method" based on simulated annealing to fine-tune the hyperparameters for the voting neural network to maximize the predictive performance. Experimental results show that AESurv outperforms seven state-of-the-art survival models on five public medical datasets in terms of the concordance index.

External IDs:dblp:conf/ijcnn/WuLCT24