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Table 3 The performance of ML based models constructed by identified six miRNA signatures on balanced training set and test set using the SMOTE algorithm

From: A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

Algorithms

Methods

Performance measures

ACC

Sensitivity

Specificity

MCC

F-score

Precision

SVMR

tenfold

0.990

0.987

0.993

0.981

0.989

0.991

Test

0.923

0.927

0.919

0.843

0.911

0.895

LR

tenfold

0.688

0.649

0.713

0.357

0.620

0.594

Test

0.612

0.473

0.716

0.194

0.510

0.553

Naïve Bayes

tenfold

0.761

0.737

0.776

0.508

0.711

0.688

 

Test

0.721

0.709

0.730

0.436

0.684

0.661

avNNet

tenfold

0.902

0.918

0.892

0.801

0.882

0.848

 

Test

0.783

0.709

0.838

0.553

0.736

0.453

KNN

tenfold

0.889

0.874

0.900

0.773

0.870

0.866

 

Test

0.775

0.782

0.770

0.547

0.748

0.717