比较6种机器学习模型在结直肠癌生存预测中的表现
Methodology for Comparing Machine Learning Algorithms for Survival Analysis
- 系统评估6种模型在含删失数据下的生存预测能力
- XGB-AFT模型表现最优，C-Index达0.7618
- 适合医学决策支持与临床生存分析研究者
本研究对六种机器学习生存分析模型（MLSA）进行了方法学比较。基于圣保罗医院癌症登记系统中近4.5万名结直肠癌患者数据，评估了随机生存森林（RSF）、生存梯度提升（GBSA）、生存SVM（SSVM）、XGBoost-Cox（XGB-Cox）、XGBoost-AFT（XGB-AFT）和LightGBM（LGBM）的性能。通过不同采样器进行超参数优化，采用一致性指数（C-Index）、C-Index IPCW、时间依赖AUC和综合Brier评分（IBS）评估模型表现。对比了模型生成的生存曲线与分类算法预测结果，并使用SHAP和置换重要性进行特征解释。XGB-AFT表现最佳（C-Index = 0.7618；IPCW = 0.7532），其次为GBSA和RSF。结果表明MLSA具有提升生存预测与辅助决策的潜力。
原文摘要 · Abstract (English)
This study presents a comparative methodological analysis of six machine learning models for survival analysis (MLSA). Using data from nearly 45,000 colorectal cancer patients in the Hospital-Based Cancer Registries of São Paulo, we evaluated Random Survival Forest (RSF), Gradient Boosting for Survival Analysis (GBSA), Survival SVM (SSVM), XGBoost-Cox (XGB-Cox), XGBoost-AFT (XGB-AFT), and LightGBM (LGBM), capable of predicting survival considering censored data. Hyperparameter optimization was performed with different samplers, and model performance was assessed using the Concordance Index (C-Index), C-Index IPCW, time-dependent AUC, and Integrated Brier Score (IBS). Survival curves produced by the models were compared with predictions from classification algorithms, and predictor interpretation was conducted using SHAP and permutation importance. XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532), followed by GBSA and RSF. The results highlight the potential and applicability of MLSA to improve survival prediction and support decision making.
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