Table 4 Prediction efficiency analysis of different models in the training set
From: Machine learning-driven identification of key risk factors for predicting depression among nurses
Classification model | AUC (95%CI) | Cut off (95%CI) | Accuracy (95%CI) | Sensitivity (95%CI) | Specificity (95%CI) | Positive predictive value (95%CI) | Negative predictive value (95%CI) | F1 score (95%CI) | Kappa (95%CI) | |
|---|---|---|---|---|---|---|---|---|---|---|
XGBoost | 0.95 (0.88–1.00) | 0.48 (0.46–0.50) | 0.85 (0.82–0.88) | 0.95 (0.91–0.98) | 0.87 (0.80–0.93) | 0.85 (0.80–0.91) | 0.87 (0.82–0.92) | 0.90 (0.86–0.93) | 0.70 (0.64–0.77) | |
logistic | 0.86 (0.74–0.97) | 0.39 (0.37–0.41) | 0.77 (0.74–0.80) | 0.87 (0.83–0.92) | 0.75 (0.69–0.82) | 0.73 (0.69–0.76) | 0.85 (0.79–0.90) | 0.79 (0.76–0.82) | 0.54 (0.47–0.60) | |
AdaBoost | 0.93 (0.85–1.00) | 0.50 (0.50–0.50) | 0.85 (0.81–0.88) | 0.95 (0.91–0.98) | 0.81 (0.75–0.88) | 0.84 (0.80–0.87) | 0.89 (0.82–0.96) | 0.89 (0.86–0.91) | 0.69 (0.62–0.76) | |
SVM | 0.88 (0.78–0.99) | 0.45 (0.41–0.49) | 0.80 (0.76–0.84) | 0.92 (0.86–0.98) | 0.77 (0.72–0.83) | 0.76 (0.71–0.80) | 0.86 (0.80–0.91) | 0.83 (0.78–0.87) | 0.59 (0.52–0.67) | |