Table 2.
The Differentiational Ability of all Models Based on 5 Feature Selection Methods and 9 Feature Classification Methods.
| Models | Test group | |||
|---|---|---|---|---|
| Sensitivity | Specificity | Accuracy | AUC | |
| OD_LDA | 0.990 | 0.833 | 0.954 | 0.953 |
| DC_LDA | 0.990 | 0.867 | 0.962 | 0.997 |
| RF_LDA | 0.990 | 0.900 | 0.969 | 0.997 |
| LASSO_LDA | 0.950 | 0.500 | 0.853 | 0.788 |
| Xgboost_LDA | 0.980 | 0.933 | 0.969 | 0.993 |
| GBDT_LDA | 0.990 | 0.867 | 0.962 | 0.980 |
| OD_SVM | 0.990 | 0.833 | 0.923 | 0.987 |
| DC_SVM | 0.990 | 0.867 | 0.962 | 0.993 |
| RF_SVM | 1.000 | 0.900 | 0.954 | 0.987 |
| LASSO_SVM | 1.000 | 0.500 | 0.792 | 0.915 |
| Xgboost_SVM | 0.970 | 0.933 | 0.938 | 0.990 |
| GBDT_SVM | 1.000 | 0.867 | 0.962 | 0.983 |
| OD_RF | 0.970 | 0.717 | 0.915 | 0.983 |
| DC_RF | 0.990 | 0.833 | 0.954 | 0.990 |
| RF_RF | 0.980 | 0.733 | 0.923 | 0.990 |
| LASSO_RF | 0.980 | 0.783 | 0.938 | 0.958 |
| Xgboost_RF | 0.990 | 0.833 | 0.954 | 0.995 |
| GBDT_RF | 0.990 | 0.783 | 0.946 | 0.997 |
| OD_Adaboost | 0.990 | 0.767 | 0.938 | 0.987 |
| DC_Adaboost | 0.961 | 0.750 | 0.915 | 0.962 |
| RF_Adaboost | 0.980 | 0.783 | 0.938 | 0.983 |
| LASSO_Adaboost | 0.960 | 0.750 | 0.915 | 0.933 |
| Xgboost_Adaboost | 0.990 | 0.800 | 0.946 | 0.990 |
| GBDT_Adaboost | 0.980 | 0.900 | 0.962 | 0.993 |
| OD_KNN | 0.960 | 0.633 | 0.884 | 0.955 |
| DC_KNN | 0.960 | 0.800 | 0.923 | 0.970 |
| RF_KNN | 0.980 | 0.767 | 0.931 | 0.970 |
| LASSO_KNN | 0.970 | 0.450 | 0.861 | 0.912 |
| Xgboost_KNN | 0.950 | 0.783 | 0.915 | 0.962 |
| GBDT_KNN | 0.980 | 0.800 | 0.938 | 0.977 |
| OD_GaussianNB | 0.980 | 0.767 | 0.931 | 0.923 |
| DC_GaussianNB | 0.970 | 0.867 | 0.946 | 0.953 |
| RF_GaussianNB | 0.910 | 0.867 | 0.900 | 0.935 |
| LASSO_GaussianNB | 0.950 | 0.467 | 0.846 | 0.907 |
| Xgboost_GaussianNB | 0.950 | 0.867 | 0.931 | 0.950 |
| GBDT_GaussianNB | 0.940 | 0.900 | 0.931 | 0.935 |
| OD_LR | 0.990 | 0.583 | 0.900 | 0.953 |
| DC_LR | 1.000 | 0.633 | 0.915 | 0.957 |
| RF_LR | 1.000 | 0.667 | 0.923 | 0.967 |
| LASSO_LR | 1.000 | 0.000 | 0.783 | 0.909 |
| Xgboost_LR | 1.000 | 0.400 | 0.869 | 0.952 |
| GBDT_LR | 1.000 | 0.483 | 0.885 | 0.990 |
| OD_GBDT | 0.960 | 0.783 | 0.923 | 0.898 |
| DC_GBDT | 0.980 | 0.850 | 0.954 | 0.932 |
| RF_GBDT | 0.960 | 0.783 | 0.923 | 0.917 |
| LASSO_GBDT | 0.980 | 0.817 | 0.946 | 0.941 |
| Xgboost_GBDT | 0.970 | 0.850 | 0.946 | 0.932 |
| GBDT_GBDT | 0.980 | 0.783 | 0.938 | 0.937 |
| OD_DT | 0.970 | 0.783 | 0.931 | 0.877 |
| DC_DT | 0.980 | 0.750 | 0.931 | 0.865 |
| RF_DT | 0.970 | 0.750 | 0.923 | 0.860 |
| LASSO_DT | 0.960 | 0.750 | 0.915 | 0.855 |
| Xgboost_DT | 0.980 | 0.750 | 0.931 | 0.865 |
| GBDT_DT | 0.990 | 0.750 | 0.938 | 0.870 |
Abbreviations: Adaboost, Adaptiveboosting; AUC, area under the curve; DC, distance correlation; DT, decision tree; GaussianNB, Gaussian Naïve Bayes; GBDT, gradient boosted decision tree; ICCA, intrahepatic cholangiocarcinoma; KNN, k-nearest neighbor; LASSO, least absolute shrinkage and selection operator; LDA, linear discriminant analysis; LR, logistic regression; RF, random forest; SVM, support vector machine; OD, original data; XGBoost, eXtreme gradient boosting.