Table 3 Results of comparison methods and proposed method.

From: A novel liver cancer diagnosis method based on patient similarity network and DenseGCN

 

Precision

Recall

F1-Score

Accuracy

AUC

pDenseGCN

0.9865

0.9865

0.9865

0.9857

0.9856

ASVM

0.937

0.9744

0.9553

0.9208

0.8531

XGBoost-AD

0.9736

0.9729

0.9732

0.9726

0.9759

MGRFE-GaRFE

0.9689

0.9397

0.9183

0.954

0.8306

ET-SVM

0.96

0.6316

0.7619

0.7945

0.8015

XOmiVAE

0.946

0.8974

0.9211

0.8537

0.8718

LDA

0.7262

0.8133

0.7673

0.7466

0.7447

RF

0.9605

0.9125

0.9359

0.937

0.9848

NB

0.8977

0.7914

0.8412

0.8452

0.8492

DT

0.9254

0.8267

0.8732

0.8767

0.8781

  1. Significant values are in bold.
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