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Table 2 The efficacy of KNN, LR, SVM, DT, MLP and XGboost in the test set

From: Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population

Metrics

KNN

LR

SVM

DT

MLP

XGboost

AU-ROC

0.81

0.82

0.78

0.73

0.79

0.83

AU-PRC

0.88

0.86

0.81

0.87

0.81

0.88

accuracy

0.81

0.81

0.78

0.78

0.77

0.80

precision

0.82

0.80

0.77

0.76

0.73

0.79

recall

0.91

0.93

0.93

0.95

1.00

0.93

F1 score

0.86

0.86

0.84

0.85

0.85

0.85

MCC

0.59

0.58

0.52

0.53

0.53

0.56

SPC

0.65

0.60

0.53

0.51

0.38

0.57

NPV

0.81

0.84

0.82

0.85

1.00

0.84

  1. AU-ROC area under the receiver operating characteristic curve, AU-PRC area under the precision-recall curve, MCC Matthews correlation coefficient, SPC specificity, NPV negative prognostic value, KNN k-nearest neighbors classifier, LR logistic regression, SVM support vector machine, DT decision tree, MLP multilayer perceptron