Table 2.
Logistic Regression | Naïve Bayes | Random Forest | Support Vector | |||||
---|---|---|---|---|---|---|---|---|
Training set (cross-validation)
| ||||||||
AUC | 0.714 | 0.037 | 0.716 | 0.039 | 0.706 | 0.037* | 0.697 | 0.042* |
Specificity | 0.867 | 0.031 | 0.783 | 0.035* | 0.886 | 0.032^ | 0.917 | 0.041^ |
Sensitivity | 0.353 | 0.058 | 0.519 | 0.064^ | 0.336 | 0.053* | 0.217 | 0.068* |
| ||||||||
Testing set
| ||||||||
AUC | 0.712 | 0.023 | 0.698 | 0.024 | 0.693 | 0.024 | 0.706 | 0.023 |
Specificity | 0.870 | 0.019 | 0.781 | 0.023 | 0.889 | 0.016 | 0.920 | 0.015 |
Sensitivity | 0.332 | 0.033 | 0.492 | 0.036 | 0.337 | 0.033 | 0.241 | 0.029 |
X2 | p val | X2 | p val | X2 | p val | X2 | p val | |
Calibration | 4.09 | 0.5 | 102.73 | <.001 | 5.04 | 0.4 | 17.02 | 0.005 |
| ||||||||
Validation set
| ||||||||
AUC | 0.719 | 0.024 | ||||||
Specificity | 0.911 | 0.016 | ||||||
Sensitivity | 0.259 | 0.036 | ||||||
X2 | p val | |||||||
Calibration | 2.38 | 0.8 |
, superior to logistic regression (p<0.05)
, inferior to logistic regression (p<0.05)
AUC, area under receiver operating characteristic curve