郑州大学学报（医学版）2026，Vol.61Issue(3)：108-112,5.DOI:10.13705/j.issn.1671-6825.2025.04.152
基于8种算法的中晚期结直肠癌死亡风险预测模型的构建
Construction of a mortality risk prediction model for patients with ad-vanced colorectal cancer based on 8 algorithms
摘要
Abstract
Aim:To construct a mortality risk prediction model for advanced colorectal cancer.Methods:A multi-stage stratified random cluster sampling method was used to select 416 patients with advanced colorectal cancer from 11 tertiary hospitals across 5 cities in Shandong Province between September 2019 and December 2021.Among them,254 patients sur-vived and 162 died within 3 years.The patients were randomly divided into a training set and a testing set at a ratio of 7∶3.Based on the training set data,LASSO regression was employed to screen predictive factors,and then eight algorithms,inclu-ding decision tree(DT),random forest(RF),LightGBM(LGBM),AdaBoost,Logistic regression,CatBoost,XGBoost(XGB),and support vector machine(SVM),were used to construct mortality risk prediction models.Results:LASSO re-gression identified 4 predictive factors:radiotherapy,chemotherapy,surgery,and metastasis status.Using these 4 factors,the 8 models were constructed with AUC(95%CI)of ROC curve in testing set as follows:0.841(0.776-0.905),0.924(0.890-0.966),0.928(0.888-0.970),0.932(0.892-0.973),0.941(0.803-0.978),0.917(0.869-0.965),0.936(0.898-0.978),and 0.889(0.823-0.955).The Logistic model demonstrated the best performance,accuracy,precision,recall rate and F1 score were 0.864,0.878,0.976 and 0.835,and SHAP value of radiotherapy,chemotherapy,surgery,and metastasis status were 0.186,0.148,0.138 and 0.052,respectively.Conclusion:The mortality risk prediction model for advanced colorectal cancer constructed using Logistic regression exhibits optimal performance.关键词
结直肠癌/死亡风险/预测模型Key words
colorectal cancer/mortality risk/predictive model分类
医药卫生引用本文复制引用
张孜,张璟,孙康宁,祝文倩,赵泽坤,王文军..基于8种算法的中晚期结直肠癌死亡风险预测模型的构建[J].郑州大学学报（医学版）,2026,61(3):108-112,5.基金项目
北京爱谱癌症患者关爱基金会项目(2019273) （2019273）
