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Table 2 Prediction accuracy of the electronic nose in the test set of machine learning algorithms

From: Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Model and parametersAccuracy (95% CI)SensitivitySpecificityPPVNPVKappaAUC (95% CI)
k-nearest neighbors (k = 5)0.77 (0.46–0.95)0.710.830.830.710.540.80 (0.54–1.00)
Naive Bayes (fL = 0, usekernel = TRUE, adjust = 1)0.77 (0.46–0.95)0.710.830.830.710.540.80 (0.54–1.00)
Decision tree (trials = 10, model = rules, window = TRUE)0.85 (0.55–0.98)0.860.830.860.830.690.85 (0.63–1.00)
Neural network (size = 3, decay = 1e-04)0.85 (0.55–0.98)0.860.830.860.830.690.85 (0.63–1.00)
Support vector machines (linear kernel) (C = 1)0.85 (0.55–0.98)0.860.830.860.830.690.85 (0.63–1.00)
Support vector machines (radial kernel) (sigma = 1.432815, C = 1)0.77 (0.46–0.95)0.710.830.830.710.540.85 (0.63–1.00)
Support vector machines (polynomial kernel) (degree = 1, scale = 0.1, C = 0.5)0.85 (0.55–0.98)0.860.830.860.830.690.85 (0.63–1.00)
Random forest (mtry = 32)0.77 (0.46–0.95)0.710.830.830.710.540.90 (0.74–1.00)
Mean value (SD)0.81 (0.04)0.79 (0.08)0.83 (0.00)0.85 (0.02)0.77 (0.06)0.62 (0.08)0.85 (0.04)
  1. PPV positive predictive value; NPV negative predictive value; AUC area under the receiver operating curve