Table 3.

Model performance with methods based on five significant SNPs

	AUC	Sensitivity	Specificity	Accuracy	Range of 95% CI of AUC
K-nearest neighbors	0.5589	0.3861	0.6591	0.533	[0.4293, 0.7101]
Logistic regression	0.6044	0.4982	0.5648	0.5346	[0.4433, 0.7368]
Naïve Bayes	0.5996	0.3921	0.7206	0.5686	[0.4571, 0.7469]
Random forest	0.5743	0.3169	0.7558	0.5535	[0.4405, 0.7233]
Support vector machine	0.5494	0.2762	0.7775	0.547	[0.4187, 0.7086]
Bayesian additive regression trees	0.5906	0.4779	0.5571	0.5211	[0.4385, 0.7211]
Boosting	0.6024	0.4723	0.5544	0.5157	[0.4584, 0.7287]
Recursive partitioning	0.5871	0.4085	0.7218	0.5778	[0.3926, 0.7048]
Fuzzy rule-based system	0.5396	0.4931	0.5006	0.4968	[0.4115, 0.6710]

AUC, sensitivity, specificity, and accuracy were its mean value in 10-fold validations. Range of 95% CI of AUC represents the range of the 95% CI of AUC in 10-fold Cross-validation. SVM represents support vector machines and Kernel Methods.