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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.
Background:
Machine learning enables complex risk prediction models, but comparative performance with statistical approaches remains context-dependent. We compared statistical and machine learning models for predicting advanced breast cancer risk.
Methods:
Using data from 968,178 women (40-74 years) undergoing 2,796,459 annual or 812,126 biennial screening mammograms (2005-2019) in the Breast Cancer Surveillance Consortium, we cross-validated models predicting advanced breast cancer within 12 months (annual) or 24 months (biennial) following screening. Models included conventional logistic regression, regularized regressions [least absolute shrinkage and selection operator (LASSO), elastic net], and machine learning methods (random forests, gradient boosting), considering a modest number of clinical and demographic predictors. Performance was assessed using calibration and area under the receiver operating characteristic curve (AUC).
Results:
Discrimination was similar across models (AUC 0.677-0.690). Calibration differences were more pronounced. Regularized regressions achieved the most favorable calibration overall and across racial and ethnic groups, with an AUC of 0.689 [95% confidence interval (CI), 0.676-0.701]. Gradient boosting showed a comparable AUC but suboptimal calibration (calibration slope 1.12; 95% CI, 1.04-1.20). Conventional logistic regression had a slightly lower AUC (0.683; 95% CI, 0.671-0.696) and a calibration slope of 0.90 (95% CI, 0.83-0.96). Regression-based approaches were generally well calibrated across racial and ethnic groups (expected-to-observed event ratio 0.96-1.03; calibration intercept -0.03 to 0.04), with some subgroup deviations in calibration slopes (<1).
Conclusions:
For predicting advanced breast cancers, regularized regression demonstrated similar discrimination and generally more favorable calibration than other approaches.
Impact:
In settings with rare outcomes and low-dimensional features, regularized regression may offer a practical balance between performance and interpretability.
