Table 3 Performance AU-ROC curve of BCRAT and ML algorithms (with standard deviation) predicting breast cancer lifetime risk from simulated datasets (n = 1200) and the US population-based sample (n = 1143)
Dataset | BCRAT | ML: random forest | ML: Logistic Regression | ML: adapt boosting | ML: Linear Model | ML: K-nearest neighbors | ML: linear discriminant | ML: quadratic discriminant | ML: MCMC GLMM |
|---|---|---|---|---|---|---|---|---|---|
A.Sim_no_signal | 0.5333 | 0.5016 (0.0231) | 0.5133 (0.0271) | 0.5067 (0.0307) | 0.5015 (0.0220) | 0.5054 (0.0211) | 0.5158 (0.0276) | 0.5133 (0.0323) | 0.5090 (0.0210) |
B.Sim_atifical_signal | 0.5261 | 0.9308 (0.0171) | 0.9417 (0.0103) | 0.9292 (0.0095) | 0.7859 (0.0197) | 0.9125 (0.0109) | 0.9312 (0.0154) | 0.9188 (0.0111) | 0.9329 (0.0087) |
C. Sim_ atifical_signal + 20% missing | 0.5068 | 0.9275 (0.0179) | 0.9217 (0.0259) | 0.9258 (0.0113) | 0.7807 (0.0227) | 0.9012 (0.0120) | 0.9213 (0.0202) | 0.9104 (0.0237) | 0.9191 (0.0210) |
D. Sim_ atifical_signal + 20% missing + imputation | 0.5035 | 0.9167 (0.0184) | 0.9300 (0.0111) | 0.9213 (0.0119) | 0.7824 (0.0200) | 0.9058 (0.0117) | 0.9275 (0.0148) | 0.9121 (0.0081) | 0.9232 (0.0099) |
US population-based sample | 0.6240 | 0.8889 (0.0201) | 0.7192 (0.0314) | 0.8828 (0.0229) | 0.6813 (0.0378) | 0.8089 (0.0217) | 0.8692 (0.0284) | 0.8675 (0.0241) | 0.8234 (0.0189) |