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Table 1 The efficacy of KNN, LR, SVM, DT, MLP and XGboost in the training 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
(95% CI) (95% CI) (95% CI) (95% CI) (95% CI) (95% CI)
AU-ROC 0.83 (0.76–0.86) 0.89 (0.83–0.93) 0.88 (0.84–0.91) 0.85 (0.77–0.88) 0.80 (0.74–0.84) 0.94 (0.89–0.98)
AU-PRC 0.93 (0.90–0.94) 0.96 (0.93–0.98) 0.95 (0.89–0.96) 0.95 (0.94–0.97) 0.92 (0.89–0.94) 0.97 (0.93–0.99)
Accuracy 0.82 (0.77–0.86) 0.83 (0.77–0.86) 0.84 (0.82–0.88) 0.89 (0.84–0.92) 0.76 (0.74–0.79) 0.91 (0.88–0.95)
Precision 0.88 (0.83–0.92) 0.86 (0.83–0.89) 0.88 (0.84–0.91) 0.92 (0.89–0.95) 0.76 (0.74–0.79) 0.95 (0.93–0.96)
Recall 0.88 (0.85–0.90) 0.91 (0.85–0.96) 0.92 (0.90–0.95) 0.94 (0.92–0.98) 0.99 (0.98–1.00) 0.93 (0.88–0.97)
F1 score 0.88 (0.84–0.90) 0.89 (0.84–0.91) 0.90 (0.88–0.92) 0.93 (0.91–0.96) 0.86 (0.85–0.88) 0.94 (0.91–0.96)
MCC 0.54 (0.39–0.64) 0.54 (0.39–0.61) 0.58 (0.51–0.69) 0.70 (0.57–0.79) 0.22 (0.10–0.40) 0.77 (0.70–0.86)
SPC 0.67 (0.53–0.80) 0.59 (0.48–0.71) 0.63 (0.50–0.76) 0.79 (0.72–0.85) 0.10 (0.05–0.26) 0.85 (0.81–0.90)
NPV 0.66 (0.56–0.71) 0.72 (0.56–0.83) 0.74 (0.69–0.79) 0.81 (0.70–0.98) 0.87 (0.54–1.00) 0.81 (0.73–0.89)
  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, 95% CI 95% confidence interval