Fig. 4

Evaluating the accuracy of models that utilize both genotype SNPs and risk factors in predicting CAD susceptibility through various machine learning techniques. 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 three feature selection strategies. Selected features were systematically evaluated with three different classification algorithms: RF, SVM and LASSO. Each classifier was trained with selected genotypes, known risk factors and PCs. A The AUC values obtained by using GWAS-driven feature selection. B The AUC values obtained by using mRMR-based feature selection. C The AUC values obtained by RF-based feature selection and by selecting the top 50 features. The red dash-dot line represents the classification accuracy achieved by using known CAD risk factors