Novel models by machine learning to predict the risk of cardiac disease-specific death in young patients with breast cancer
Independent CDSD risk factors for young BC patients were identified, and machine-learning prognostic models were constructed to predict their CDSD risks. Our validation results indicated that the predicted probability of our XGBoost model agrees well with the actual CDSD risks, and it can help recognize high-risk populations and therefore develop effective cardioprotection strategies. Hopefully, our findings can support the growth of the new field of cardio-oncology.
Authors
- Handong Li (ORCID: https://orcid.org/0000-0003-3613-7327)
- Yi Li (ORCID: https://orcid.org/0000-0002-5189-0627)
- Xuan Ye
- Zhigang Zhu
- Y. Qiu
Institutions
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Guangdong Academy of Medical Sciences (CN)
- Shuguang Hospital (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
- South China University of Technology (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Discover Oncology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1007/s12672-024-01676-9
- Citations
- 2
- Primary Topic
- Chemotherapy-induced cardiotoxicity and mitigation
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Natural Science Foundation of China
- Guangzhou Municipal Science and Technology Project