Ensemble model and empirical analysis of breast cancer diagnosis based on Stacking

CHENG Mushuang; WANG Guoqiang

doi:10.12299/jsues.24-0147

Volume 39 Issue 3

Sep. 2025

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CHENG Mushuang, WANG Guoqiang. Ensemble model and empirical analysis of breast cancer diagnosis based on Stacking[J]. Journal of Shanghai University of Engineering Science, 2025, 39(3): 360-365. doi: 10.12299/jsues.24-0147

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CHENG Mushuang, WANG Guoqiang. Ensemble model and empirical analysis of breast cancer diagnosis based on Stacking[J]. Journal of Shanghai University of Engineering Science, 2025, 39(3): 360-365. doi: 10.12299/jsues.24-0147

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Ensemble model and empirical analysis of breast cancer diagnosis based on Stacking

doi: 10.12299/jsues.24-0147

CHENG Mushuang,
WANG Guoqiang^,

School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, 201620, China

Received Date: 2024-05-23
Available Online: 2025-12-22
Publish Date: 2025-09-30

Abstract

Abstract

The early diagnosis of breast cancer can significantly improve the possibility of cure. In recent years, the boom of big data and artificial intelligence technology provides technical support for early diagnosis of many diseases, including breast cancer. In order to improve the accuracy of breast cancer diagnosis, an improved Stacking integration model based on area under curve (AUC) was constructed. Firstly, an AdaBoost ensemble model based on $v$-SVM is constructed and used as a meta learner for Stacking. Secondly, the overall AUC values of each base learner were used to weight the training results of each base learner, and the weighted results were used as the training set of the meta learner for training. Finally, empirical analysis was conducted on the WDBC and WBC datasets. The experimental results show that the Stacking ensemble model based on AUC improvement can achieve high accuracy on two datasets, provide doctors with more refined and personalized diagnostic criteria, thereby achieving the goal of earlier intervention and more efficient treatment.
- breast cancer diagnosis,
- area under curve (AUC),
- Stacking ensemble model,
- machine learning

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References(14)

References

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