Recurrence Prediction Model of DLBCL Patients within 2 Years based on SMOTE-ENN Combined with Improved Dynamic Ensemble Selection Algorithm
10.11783/j.issn.1002-3674.2025.01.009
- VernacularTitle:基于SMOTE-ENN结合改进动态集成选择算法构建DLBCL患者2年内复发预测模型
- Author:
Gaoyuan ZHANG
1
;
Ruiqing ZHAO
;
Yanbo ZHANG
Author Information
1. 山西医科大学公共卫生学院卫生统计教研室(030001)
- Publication Type:Journal Article
- Keywords:
Diffuse large B-cell lymphoma;
Recurrence prediction;
Category imbalance;
Dynamic ensemble selection
- From:
Chinese Journal of Health Statistics
2025;42(1):50-55,61
- CountryChina
- Language:Chinese
-
Abstract:
Objective The prediction model of recurrence within two years after complete remission of diffuse large B-cell lymphoma(DLBCL)patients was constructed based on frienemy indecision region dynamic ensemble selection(FIRE-DES)to provide decision-making basis for the treatment of patients.Methods To collect data of 498 patients who achieved complete response after treatment from January 2010 to January 2020 in a Grade-A hospital in Shanxi Province.A FIRE-DES combination prediction model based on four common category-disequilibrium treatment methods was constructed and compared with five traditional single classifiers and two integrated classifiers.Results Among the four categories of unbalance algorithms,synthetic minority oversampling technique and edited nearest neighbor(SMOTE-ENN)algorithm has obtained the optimal classification performance.On this basis,the classification effect of dynamic ensemble selection performance(DESP),K-nearest oracle union(KNORAU)and meta-learning for dynamic ensemble selection(META-DES)dynamic integration selection algorithms is obviously superior to the traditional single classifier and ensemble classifier model.The classification effect of the improved DESP,KNORAU and META-DES dynamic selection algorithms based on Frienemy Indecision Region is further improved.The classification performance of FIRE-META-DES was the best(Accuracy=0.909,Precision=0.906,Recall=0.967,AUC=0.879,F1-Score=0.936,Brier Score=0.088).Conclusion Aiming at the actual DLBCL data set,SMOTE-ENN+FIRE-META-DES combined prediction model for recurrence used in this paper achieves the optimal performance and low computational complexity,which can provide a strong reference for DLBCL recurrence prediction.
