Table 5. Classifier model prediction performance on CPOnly (controls and culture positive cases) for fixed sensitivity ratio of 0.8.
Model | Specificity | PPV | NPV |
---|---|---|---|
AdaBoost | 0.71 [0.64, 0.81] | 0.23 [0.18, 0.30] | 0.97 [0.97, 0.98] |
Gradient boosting | 0.69 [0.58, 0.91] | 0.23 [0.16, 0.47] | 0.98 [0.97, 0.99] |
Gaussian process | 0.53 [0.32, 0.80] | 0.16 [0.11, 0.29] | 0.97 [0.95, 0.98] |
K-nearest neighbors | 0.20 [0, 0.77] | 0.13 [0.09, 0.26] | 0.29 [0, 1] |
Logistic regression | 0.71 [0.61, 0.82] | 0.23 [0.17, 0.31] | 0.97 [0.97, 0.98] |
Naïve Bayes | 0.69 [0.49, 0.77] | 0.22 [0.14, 0.28] | 0.98 [0.96, 0.99] |
Random forest | 0.71 [0.62, 0.83] | 0.23 [0.19, 0.32] | 0.98 [0.97, 0.99] |
Support vector machine* | 0.72 [0.63, 0.83] | 0.23 [0.18, 0.32] | 0.98 [0.97, 0.98] |
PPV: positive predictive value; NPV: negative predictive value
*The radial basis function kernel was used for the support vector machine