Table 2 For case 2: Average F1 scores, precisions, and recalls of the CHD risk prediction models built on SASMOTES, SASMOTE without visible neighbors, SASMOTE without inspections, B-SMOTE, SMOTE, and original datasets. The values in brackets represent the standard deviation (Std) in 5-fold cross validation. The proposed SASMOTE performs better on the average F1 score, and the SMOTE performs best on the average precision. The original dataset performs best on the average recall

From: A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare