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An Improved Training Algorithm Based on Ensemble Penalized Cox Regression for Predicting Absolute Cancer Risk

China CDC weekly 2023年第9期5卷 206-212,I0009-I0014页

作者： Liyuan Liu Fu Yang Yeye Fan chunyu kao Fei Wang Lixiang Yu Yong He Jiadong Ji Zhigang Yu Department of Breast Surgery The Second HospitalCheeloo College of MedicineShandong UniversityJinan CityShandong ProvinceChina School of Mathematics Shandong UniversityJinan CityShandong ProvinceChina Zhongtai Securities Institute for Financial Studies Shandong UniversityJinan CityShandong ProvinceChina Institute of Translational Medicine of Breast Disease Prevention and Treatment Shandong UniversityJinan CityShandong ProvinceChina.

Introduction:Biases in cancer incidence characteristics have led to significant imbalances in databases constructed by prospective cohort *** they use imbalanced databases,many traditional algorithms for training cancer risk prediction models perform ***:To improve prediction performance,we introduced a Bagging ensemble framework to an absolute risk model based on ensemble penalized Cox regression(EPCR).We then tested whether the EPCR model outperformed other traditional regression models by varying the censoring rate of the simulated ***:Six different simulation studies were performed with 100 *** assess model performance,we calculated mean false discovery rate,false omission rate,true positive rate,true negative rate,and the areas under the receiver operating characteristic curve(AUC)*** found that the EPCR procedure could reduce the false discovery rate(FDR)for important variables at the same true positive rate(TPR),thereby achieving more accurate variable *** addition,we used the EPCR procedure to build a breast cancer risk prediction model based on the Breast Cancer Cohort Study in Chinese Women *** for 3-and 5-year predictions were 0.691 and 0.642,representing improvements of 0.189 and 0.117 over the classical Gail model,***:We conclude that the EPCR procedure can overcome challenges posed by imbalanced data and improve the performance of cancer risk assessment tools.

关键词： Cancer false prediction

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