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Table 1 Macro-averaging AUC, F1-score, precision, recall and accuracy of four typical classifiers based on negative samples selected by HCNS-ADR and RGNS

From: Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases

Classifier Negative Samples Macro_AUC Macro_F1 Macro_Precision Macro_Recall Macro_Accuracy
SVM HCNS-ADR 0.994 0.973 0.985 0.963 0.975
SVM RGNS 0.946 0.887 0.896 0.888 0.893
Logistic regression HCNS-ADR 0.998 0.980 0.991 0.971 0.981
Logistic regression RGNS 0.963 0.903 0.898 0.913 0.905
KNN HCNS-ADR 0.983 0.920 0.972 0.883 0.936
KNN RGNS 0.923 0.859 0.850 0.877 0.862
Random forest HCNS-ADR 0.943 0.840 0.928 0.781 0.861
Random forest RGNS 0.787 0.713 0.753 0.700 0.717