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Table 4 The results of four indicators—precision, recall, F1-score, support in validation set

From: CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

Risk groups

Indicators

KNN

SVM

XGBoost

RF

LR

DT

Low

Precision

0.9

0.89

0.78

0.67

0.89

0.5

Recall

0.9

0.8

0.7

0.8

0.8

0.3

F1-score

0.9

0.84

0.74

0.73

0.84

0.37

Support

10

10

10

10

10

10

High

Precision

0.86

0.75

0.62

0.6

0.75

0.36

Recall

0.86

0.86

0.71

0.43

0.86

0.57

F1-score

0.86

0.8

0.67

0.5

0.8

0.44

Support

7

7

7

7

7

7

  1. KNN k-nearest neighbor, SVM support vector machine, XGBoost eXtreme Gradient Boosting, RF random forest, LR logistic regression, DT decision tree