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. 2023 Apr 3;14:1140755. doi: 10.3389/fimmu.2023.1140755

Table 2.

Performance of machine learning models.

Model AUC ACC (%) Best cutoff Youden index (%) Sensitivity (%) Specificity (%) F1 score PPV (%) NPV (%)
CatBoost 0.83 75 0.195 50 75 75 0.56 44 92
GBDT 0.82 71 0.16 48 79 69 0.53 40 93
LightGBM 0.82 74 0.183 49 75 74 0.55 43 92
AdaBoost 0.82 79 0.494 48 65 83 0.57 51 90
Random Forest 0.82 78 0.28 47 66 81 0.55 48 90
XGBoost 0.81 77 0.204 47 68 79 0.55 46 90
KNN 0.8 72 0.176 45 73 72 0.52 41 91
MLP 0.79 73 0.162 43 70 73 0.52 41 90
LR 0.79 73 0.201 44 71 74 0.52 41 90
NaiveBayes 0.76 68 0.092 41 74 67 0.49 37 91
SVM 0.76 74 0.149 45 69 75 0.53 43 90

CatBoost, categorical boosting; GBDT, gradient boosting decision tree; LightGBM, light gradient boosting; AdaBoost, adaptive boosting; XGBooST, extremely gradient boosting; KNN, K-nearest neighbor; MLP, multilayer perceptron; LR, logistic regression. SVM, support vector machine; ACC, accuracy, PPV, positive predictive value; NPV, negative predictive value.