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Table 4 Performance of different machine learning algorithms in the classification of moderate COPD and severe COPD

From: Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease

Model Accuracy Sensitivity Specificity AUC F1-Score Kappa
SVM 94.26 97.32 89.93 97.54 94.25 88.16
(92.70–95.85) (96.83–98.01) (87.79–92.16) (96.96–98.62) (93.16–95.03) (85.85–90.42)
Bayes 89.37 99.17 79.61 97.75 90.30 78.74
(87.70–91.04) (98.74–99.61) (76.48–82.73) (97.06–98.44) (88.68–91.92) (75.52–81.96)
Decision Tree 70.63 72.69 68.56 68.04 71.20 41.24
(69.07–72.18) (70.13–75.25) (65.09–72.04) (66.56–69.52) (70.17–72.22) (37.79–44.69)
DBN 83.75 87.80 79.66 85.01 84.42 67.45
(80.71–86.78) (83.72–91.87) (74.25–85.06) (82.01–88.01) (81.27–87.57) (61.40–73.49)