Fig. 4From: A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcareFor case 1: A F1 scores, B precisions and C recalls of the risk gene prediction models built on SASMOTE, SASMOTE without invisible neighbors (SASMOTE w/o visible), SASMOTE without inspections (SASMOTE w/o inspections), B-SMOTE, SMOTE, and without data resampling under different balanced ratios. The proposed SASMOTE performs better on the average F1 score under most imbalanced ratios. The original dataset performs best on the average precision, but worst on the recall and F1 scoreBack to article page