Table 2.
Diagnostic performance of each model for NSCLC bone metastasis in internal validation cohort.
| Model | Accuracy | AUC (95% CI) | Kappa | Sensitivity % | Specitivity % | Youden index | F1 score |
|---|---|---|---|---|---|---|---|
| DT | 0.85 | 0.89 (0.82–0.93) | 0.70 | 87.18% | 82.35% | 0.70 | 0.87 |
| Logistic | 0.69 | 0.80 (0.76–0.83) | 0.37 | 87.76% | 62.75% | 0.51 | 0.88 |
| MLP | 0.67 | 0.71 (0.67–0.76) | 0.17 | 42.86% | 75.16% | 0.18 | 0.43 |
| RF | 0.94 | 0.98 (0.95–0.99) | 0.84 | 91.84% | 94.12% | 0.86 | 0.92 |
| SVM | 0.79 | 0.88 (0.81–0.94) | 0.58 | 87.18% | 70.59% | 0.58 | 0.87 |
| Xgboost | 0.93 | 0.97 (0.93–0.99) | 0.80 | 89.80% | 93.46% | 0.83 | 0.90 |
NSCLC: Non-Small Cell Lung Cancer; AUC: Area Under Curve; CI: Confidence Interval; DT: Decision tree model; Logistic: Logistic regression model; MLP: Multilayer perceptron model; RF: Random forest model; SVM: Support vector machine model; Xgboost: Extreme gradient boosting model.