Table 2 Performance metrics of different machine learning approaches
From: Prediction of depressive disorder using machine learning approaches: findings from the NHANES
Model | Accuracy | Sensitivity | Specificity | Precision | AUC | F1_Score |
|---|---|---|---|---|---|---|
LR | 0.66 (0.59–0.73) | 0.64 (0.55–0.73) | 0.68 (0.59–0.77) | 0.66 (0.57–0.75) | 0.66 (0.59–0.72) | 0.65 (0.57–0.72) |
RF | 0.65 (0.59–0.72) | 0.60 (0.50–0.69) | 0.71 (0.61–0.79) | 0.67 (0.57–0.77) | 0.65 (0.59–0.72) | 0.63 (0.55–0.71) |
NB | 0.68 (0.62–0.75) | 0.70 (0.61–0.78) | 0.67 (0.57–0.76) | 0.68 (0.59–0.77) | 0.68 (0.62–0.75) | 0.69 (0.62–0.76) |
SVM | 0.68 (0.61–0.75) | 0.65 (0.55–0.74) | 0.72 (0.63–0.80) | 0.69 (0.60–0.78) | 0.68 (0.62–0.75) | 0.67 (0.59–0.74) |
XGBoost | 0.69 (0.63–0.75) | 0.68 (0.59–0.77) | 0.71 (0.62–0.79) | 0.70 (0.60–0.79) | 0.69 (0.63–0.75) | 0.69 (0.61–0.76) |
LightGBM | 0.62 (0.55–0.69) | 0.64 (0.54–0.73) | 0.61 (0.51–0.70) | 0.62 (0.52–0.71) | 0.62 (0.55–0.69) | 0.63 (0.55–0.70) |