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Table 4 Performance AU-ROC curve of the BOADICEA model and ML algorithms (with standard deviation) predicting breast cancer lifetime risk from simulated datasets (n = 2500) and Swiss clinic-based sample (n = 112,587 women from 2481 families)

From: Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

DatasetBOADICEA modelML: random forestML: logistic regressionML: adapt boostingML: linear modelML: K-nearest neighborsML: linear discriminantML: quadratic discriminantML: MCMC GLMM
A.Sim_no_signal0.51030.5020 (0.0197)0.5093 (0.0210)0.5029 (0.0177)0.5151 (0.0190)0.5254 (0.0199)0.5094 (0.0241)0.5002 (0.0216)0.5075 (0.0201)
B.Sim_ atifical_signal0.53920.9101 (0.0148)0.9233 (0.0172)0.9321 (0.0122)0.6659 (0.0164)0.9301 (0.0159)0.9109 (0.0187)0.9244 (0.0166)0.9219 (0.0151)
C.Sim_ atifical_signal + 20% missing0.50220.8977 (0.0183)0.9100 (0.0293)0.9291 (0.0156)0.6407 (0.0257)0.9232 (0.0180)0.8982 (0.0276)0.9209 (0.0297)0.9088 (0.0219)
D.Sim_ atifical_signal + 20% missing +imputation0.51150.9028 (0.0127)0.9203 (0.0157)0.9299 (0.0110)0.6463 (0.0147)0.9276 (0.0140)0.9035 (0.0159)0.9220 (0.0141)0.9154 (0.0137)
Swiss clinic-based sample0.59310.8535 (0.0214)0.8271 (0.0189)0.9017 (0.0162)0.6921 (0.0202)0.8377 (0.0156)0.7899 (0.0188)0.8369 (0.0192)0.8932 (0.0149)