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Performance of Statistical and Machine Learning Risk Prediction Models for Advanced Breast Cancer
Shuai Chen1, Karla Kerlikowske2,3,4, Yu-Ru Su5,6
1Department of Public Health Sciences, University of California, Davis, California.
Summary
Regularized regression models showed similar accuracy but better calibration for predicting advanced breast cancer risk compared to machine learning methods. This approach offers a practical balance of performance and interpretability.
Area of Science:
- Biostatistics
- Medical Informatics
- Oncology
Background:
- Machine learning models offer complex risk prediction, but their performance relative to statistical methods is context-specific.
- Advanced breast cancer risk prediction requires robust and reliable models.
Purpose of the Study:
- To compare the performance of statistical and machine learning models in predicting advanced breast cancer risk.
- To evaluate model calibration and discrimination using large-scale screening mammography data.
Main Methods:
- Utilized data from 968,178 women (aged 40-74) from the Breast Cancer Surveillance Consortium (2005-2019).
- Cross-validated logistic regression, regularized regressions (LASSO, Elastic net), random forests, and gradient boosting models.
- Assessed model performance using calibration and area under the receiver operating characteristic curve (AUC).
Main Results:
- All models demonstrated similar discrimination (AUC 0.677-0.690).
- Regularized regressions achieved the most favorable calibration across all groups (AUC 0.689).
- Gradient boosting showed comparable AUC but suboptimal calibration, while conventional logistic regression had slightly lower AUC and calibration.
Conclusions:
- Regularized regression models provide similar discrimination and superior calibration for advanced breast cancer risk prediction.
- Regularized regression offers a practical balance of performance and interpretability, especially for rare outcomes with limited features.
