Fig. 3

Classification performance obtained by using PRS, standard classification algorithms and known CAD risk factors. Bar-plots showing the AUC values computed with tenfold cross-validation. Error bars are used to assess model stability, while the different subplots aim to highlight the performance of PRS methods, and ML approaches using genotype data or known risk factors as predictors. A The AUC values obtained by combining PRS scores with logistic regression. B The AUC values obtained by using standard machine learning algorithms. C The AUC values obtained by using known risk factors as input to standard machine learning algorithms