An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review
- DOI
- 10.2196/35750
- Published
- 2022-12-29
- Container
- JMIR Public Health and Surveillance
- Publisher
- JMIR Publications Inc.
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.2196/35750,
title = {An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review},
author = {Ying Gao and Shu Li and Yujing Jin and Lengxiao Zhou and Shaomei Sun and Xiaoqian Xu and Shuqian Li and Hongxi Yang and Qing Zhang and Yaogang Wang},
year = {2022},
journal = {JMIR Public Health and Surveillance},
doi = {10.2196/35750},
url = {https://doi.org/10.2196/35750}
}RIS
TY - JOUR TI - An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review AU - Ying Gao AU - Shu Li AU - Yujing Jin AU - Lengxiao Zhou AU - Shaomei Sun AU - Xiaoqian Xu AU - Shuqian Li AU - Hongxi Yang AU - Qing Zhang AU - Yaogang Wang PY - 2022 JO - JMIR Public Health and Surveillance DO - 10.2196/35750 UR - https://doi.org/10.2196/35750 ER -
APA
Gao, Y., Li, S., Jin, Y., Zhou, L., Sun, S., Xu, X., Li, S., Yang, H., Zhang, Q., & Wang, Y. (2022). An Assessment of the Predictive Performance of Current Machine Learning–Based Breast Cancer Risk Prediction Models: Systematic Review. JMIR Public Health and Surveillance. https://doi.org/10.2196/35750
Source records
- crossref · retrieved 2026-09-24T22:27:18.749Z