Dataset | Evaluation metric | Machine learning models | ||||
---|---|---|---|---|---|---|
Logistic regression | Naïve bayes | Random forest | XGBoost | SVM RBF | ||
Test 1 evaluation | AUC | 0.990 | 0.986 | 0.993 | 0.994 | 0.991 |
Sensitivity | 0.928 | 0.936 | 0.928 | 0.936 | 0.936 | |
Specificity | 0.963 | 0.960 | 0.963 | 0.963 | 0.963 | |
F1 score | 0.913 | 0.914 | 0.913 | 0.918 | 0.918 | |
Accuracy | 0.945 | 0.948 | 0.945 | 0.949 | 0.949 | |
Avg. Precision | 0.970 | 0.951 | 0.975 | 0.976 | 0.970 | |
Test 2 evaluation | AUC | 0.988 | 0.982 | 0.989 | 0.992 | 0.990 |
Sensitivity | 0.937 | 0.945 | 0.945 | 0.945 | 0.937 | |
Specificity | 0.961 | 0.961 | 0.963 | 0.963 | 0.963 | |
F1 Score | 0.915 | 0.920 | 0.923 | 0.923 | 0.919 | |
Accuracy | 0.949 | 0.953 | 0.954 | 0.954 | 0.950 | |
Avg. Precision | 0.964 | 0.960 | 0.968 | 0.972 | 0.965 | |
Test 3 evaluation | AUC | 0.987 | 0.972 | 0.987 | 0.991 | 0.986 |
Sensitivity | 0.913 | 0.921 | 0.921 | 0.937 | 0.921 | |
Specificity | 0.966 | 0.958 | 0.966 | 0.966 | 0.966 | |
F1 Score | 0.910 | 0.903 | 0.914 | 0.922 | 0.914 | |
Accuracy | 0.940 | 0.940 | 0.944 | 0.952 | 0.944 | |
Avg. Precision | 0.961 | 0.936 | 0.958 | 0.965 | 0.946 |