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Table 5 The result of a repeated 10-fold cross-validation experiment using naive Bayes, support vector machine (SVM), and C4.5 decision tree with the wrapper-based feature selection method.

From: A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

Algorithm

AUC

Sensitivity

Specificity

Number of SNPs

Naive Bayes

0.70 ± 0.16

0.64 ± 0.20

0.63 ± 0.19

8

SVM with linear kernel

0.63 ± 0.14

0.71 ± 0.20

0.55 ± 0.21

9

SVM with polynomial kernel

0.63 ± 0.12

0.43 ± 0.20

0.82 ± 0.16

12

SVM with sigmoid kernel

0.64 ± 0.13

0.59 ± 0.21

0.70 ± 0.18

6

SVM with Gaussian radial basis function kernel

0.63 ± 0.13

0.60 ± 0.20

0.66 ± 0.19

7

C4.5 decision tree

0.59 ± 0.16

0.65 ± 0.21

0.55 ± 0.22

6

  1. AUC = the area under the receiver operating characteristic curve, SNP = single nucleotide polymorphism.
  2. Data are presented as mean ± standard deviation.