Table 2.
Comparison of ROC AUC and Brier scores.
| Cohorts | Predictive models | Training AUC (95% CI) | Test AUC (95% CI) | Training Brier (95% CI) | Test Brier (95% CI) | ||||
|---|---|---|---|---|---|---|---|---|---|
| Sarcopenia-1 | Logistic Regression | 76.61 | (74.42, 78.80) | 71.59 | (71.51, 71.66) | 0.131 | (0.118, 0.144) | 0.154 | (0.139, 0.169) |
| Support Vector Machine | 79.92 | (74.65, 85.19) | 71.17 | (69.28, 73.07) | 0.135 | (0.123, 0.146) | 0.169 | (0.160, 0.178) | |
| Multi-Layer Perceptron | 75.20 | (67.42, 82.99) | 71.48 | (71.00, 71.97) | 0.134 | (0.125, 0.143) | 0.160 | (0.149, 0.171) | |
| Random Forest | 74.06 | (72.65, 75.46) | 69.09 | (67.97, 70.22) | 0.132 | (0.129, 0.136) | 0.156 | (0.149, 0.162) | |
| Gradient Boosting | 77.63 | (76.67, 78.59) | 69.56 | (69.28, 69.83) | 0.165 | (0.163, 0.167) | 0.203 | (0.200, 0.206) | |
| XGBoost | 73.68 | (72.18, 75.18) | 70.19 | (69.18, 71.19) | 0.130 | (0.130, 0.131) | 0.148 | (0.146, 0.150) | |
| Sarcopenia-2 | Logistic Regression | 96.46 | (95.13, 97.79) | 91.44 | (91.28, 91.60) | 0.034 | (0.018, 0.051) | 0.047 | (0.028, 0.067) |
| Support Vector Machine | 95.56 | (92.18, 98.95) | 90.81 | (88.41, 93.20) | 0.029 | (0.022, 0.036) | 0.042 | (0.037, 0.047) | |
| Multi-Layer Perceptron | 94.20 | (89.94, 98.46) | 88.50 | (85.83, 91.18) | 0.035 | (0.021, 0.050) | 0.047 | (0.039, 0.056) | |
| Random Forest | 96.18 | (95.92, 96.44) | 90.04 | (88.16, 91.92) | 0.031 | (0.030, 0.032) | 0.043 | (0.041, 0.044) | |
| Gradient Boosting | 94.63 | (89.67, 99.60) | 86.18 | (79.56, 92.80) | 0.029 | (0.018, 0.040) | 0.048 | (0.035, 0.060) | |
| XGBoost | 92.89 | (90.94, 94.84) | 89.56 | (82.45, 96.68) | 0.033 | (0.027, 0.040) | 0.045 | (0.035, 0.056) | |
For Brier score, lower is better and <0.11 would be excellent. (The columns marked Test area under the curve [AUC] and Test Brier correspond to the graphs in Figure 1.)