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Table 3 Performance of different prediction models generated by sixfold cross-validation on the training data set

From: Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

Classifier

Precision

Recall

F-Measure

MCC

AUC

auPRC

TP rate

FP rate

Kappa statistic

NB

0.869

0.869

0.869

0.729

0.925

0.920

0.869

0.143

0.728

SMO

0.863

0.862

0.862

0.715

0.860

0.814

0.862

0.142

0.715

RF

0.768

0.769

0.768

0.518

0.887

0.881

0.769

0.259

0.516

  1. NB Naive Bayes, SMO sequential minimal optimization, RF random forest, MCC Matthews correlation coefficient, AUC area under receiver operating curve, auPRC area under precision recall curve, TP true positive, FP false positive
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BMC Cardiovascular Disorders

ISSN: 1471-2261

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