Application of Decision Tree-based Ensemble Learning in Cancer Risk Stratification
The paper proposes a cancer risk stratification method based on decision tree ensemble learning,and applies it on the TCGA cancer data set.Decision tree ensemble-based survival analysis model is constructed on the preprocessed data set,and the optimal hyperparameter by is chosen Bayesian optimization method.C-index and time-dependent AUC evaluation values show that random forest(RSF)and gradient boosting tree(GBM)are better than other algorithms.It shows that the cancer risk stratifica-tion method based on RSF and GBM risk scores plays a significant role in identifying high-risk and low-risk patients.
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