A Machine Learning Model for Predicting Breast Cancer Recurrence and Supporting Personalized Treatment Decisions Through Comprehensive Feature Selection and Explainable Ensemble Learning
This LASSO-enhanced ensemble model significantly improves the accuracy and interpretability of breast cancer recurrence prediction. By identifying individualized recurrence risks through SHAP analysis, the model supports more precise, data-driven clinical decision-making. These findings demonstrate its potential as a clinical decision support tool for guiding personalized treatment strategies, contributing to more effective breast cancer management.
Authors
- Pei‐Ju Chao (ORCID: https://orcid.org/0000-0003-4155-853X)
- Shyh‐An Yeh (ORCID: https://orcid.org/0000-0002-0580-9111)
- Chia-Hui Chen
- Tsair-Fwu Lee (ORCID: https://orcid.org/0000-0002-3112-3594)
- Jun‐Ping Shiau (ORCID: https://orcid.org/0000-0003-0443-962X)
- Yun Wu (ORCID: https://orcid.org/0000-0002-6926-777X)
- Yu-Jie Huang (ORCID: https://orcid.org/0000-0002-5729-3305)
- Hui‐Chun Chen
- Cheng‐Shie Wuu
Institutions
- University of Science and Technology (YE)
- Kaohsiung Medical University (TW)
- Chang Gung University (TW)
- Kaohsiung Medical University Chung-Ho Memorial Hospital (TW)
- E-Da Hospital (TW)
- Kaohsiung Chang Gung Memorial Hospital (TW)
- National Kaohsiung University of Science and Technology (TW)
- Columbia University (US)
- I-Shou University (TW)
Publication Details
- Journal
- Cancer Management and Research
- Published
- 2025-05-01
- DOI
- https://doi.org/10.2147/cmar.s514693
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- Great Britain China Centre
- National Science and Technology Council