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Table 3 Performance of diagnostic support models constructed using combinations of candidate biomarkers with various classifiers

From: Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis

Features

Classifier

Accuracy

Sensitivity

Precision

AUC

PTPRC+ASUN

Decision tree

91.49 %

91.5 %

97.7 %

0.943

PTPRC+ASUN+DHX29

Random Forest

93.62 %

93.6 %

93.6 %

0.982

PTPRC+ASUN+DHX29

SVM

95.74 %

95.7 %

96.2 %

0.969

PTPRC+ASUN+DHX29

Naïve Bayes

97.87 %

97.9 %

98 %

0.979

  1. Sensitivity: TP/(TP+FN); Precision: TP/(TP+FP); performance was evaluated by 5-fold cross-validation
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BMC Bioinformatics

ISSN: 1471-2105

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