Fig. 5

Comparing the accuracy of different classification models, feature selection techniques and predictors. Bar-plots showing the AUC values computed with tenfold cross-validation by using two main classification algorithms (RF and LASSO) and different sets of features (or predictors). Feature sets include known risk factors, all SNPs, PRS, and the top 50 genomic variants selected by RF, mRMR and GWAS results. Moreover, classification models were trained with both genotype data and a combination of genotype data and risk factor. Classifiers annotated with top PRS are trained with the best performing PRS method, which is lassosum. Error bars are used to assess model stability