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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

MetricsKNNLRSVMDTMLPXGboost
AU-ROC0.810.820.780.730.790.83
AU-PRC0.880.860.810.870.810.88
accuracy0.810.810.780.780.770.80
precision0.820.800.770.760.730.79
recall0.910.930.930.951.000.93
F1 score0.860.860.840.850.850.85
MCC0.590.580.520.530.530.56
SPC0.650.600.530.510.380.57
NPV0.810.840.820.851.000.84
  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