Table 2 Performance of the new and Korean undiagnosed diabetes screening method in the development and validation datasets.
Model | Screening method | Feature | AUC (95% CI) | Youden index | Sensitivity (%) | Specificity (%) | PPV | NPV | PLR | NLR | |
---|---|---|---|---|---|---|---|---|---|---|---|
Train & Internal Validation Set | Park* | Risk score | Sex, Age, WC, RHR | 0.745 (0.717 to 0.773) | 37.00 | 70 | 66 | 0.08 | 0.98 | 2.09 | 0.45 |
Logistic Regression | Logistic Regression | 0.780 (0.754 to 0.806) | 41.90 | 80.94 | 60.92 | 0.09 | 0.98 | 2.07 | 0.31 | ||
Random Forest | Random Forest Classifier | 0.781 (0.756 to 0.806) | 41.20 | 84.60 | 56.60 | 0.08 | 0.99 | 2.1 | 0.16 | ||
LGBM | LightGBM Classifier | 0.778 (0.752 to 0.804) | 41.70 | 82.00 | 61.60 | 0.08 | 0.99 | 2.14 | 0.29 | ||
XGB | XGBoost Classifier | 0.778 (0.752 to 0.804) | 41.50 | 82.40 | 59.10 | 0.08 | 0.98 | 2.12 | 0.23 | ||
Ada | AdaBoost Classifier | 0.780 (0.754 to 0.806) | 41.80 | 82.60 | 59.20 | 0.08 | 0.99 | 2.03 | 0.29 | ||
External Validation set | Park* | Risk score | Sex, Age, WC, RHR | 0.740 (0.721 to 0.759) | 35.00 | 75 | 61 | 0.09 | 0.98 | 1.9 | 0.42 |
Logistic Regression | Logistic Regression | 0.786 (0.77 to 0.802) | 43.30 | 80.25 | 63.04 | 0.11 | 0.98 | 2.2 | 0.31 | ||
Random Forest | Random Forest Classifier | 0.788 (0.772 to 0.804) | 44.00 | 87.40 | 56.50 | 0.18 | 0.99 | 2.01 | 0.22 | ||
LGBM | LightGBM Classifier | 0.788 (0.772 to 0.804) | 43.70 | 82.90 | 60.80 | 0.1 | 0.99 | 2.12 | 0.28 | ||
XGB | XGBoost Classifier | 0.788 (0.772 to 0.804) | 44.00 | 85.80 | 58.20 | 0.1 | 0.99 | 2.05 | 0.24 | ||
Ada | AdaBoost Classifier | 0.779 (0.762 to 0.796) | 42.40 | 81.20 | 61.30 | 0.1 | 0.98 | 2.1 | 0.31 |