计算机与数字工程2024，Vol.52Issue(1)：169-173,189,6.DOI:10.3969/j.issn.1672-9722.2024.01.027
基于决策树集成学习在癌症风险分层中的应用
Application of Decision Tree-based Ensemble Learning in Cancer Risk Stratification
摘要
Abstract
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.关键词
集成学习/随机生存森林/梯度提升树/生存分析/风险分层Key words
ensemble learning/random survival forest/gradient boosting tree/survival analysis/risk stratification分类
信息技术与安全科学引用本文复制引用
殷清燕,车露美,刘星宇..基于决策树集成学习在癌症风险分层中的应用[J].计算机与数字工程,2024,52(1):169-173,189,6.基金项目
西安市科学技术局项目(编号:2019218114GXRC017CG018-GXYD17.6)资助. （编号:2019218114GXRC017CG018-GXYD17.6）
