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 |