Table 2.

Performance measures of different machine learning algorithms for classifications of different types of cardiomyopathies and subtype recognition using the top 1,000 high-variance gene features

	Accuracy	AUC	Sensitivity	Specificity	Precision	F1
NF vs. DCM
DT	0.84 ± 0.06	0.83 ± 0.08	0.76 ± 0.13	0.92 ± 0.07	0.90 ± 0.08	0.81 ± 0.08
ENet	0.92 ± 0.05	0.96 ± 0.06	0.93 ± 0.07	0.91 ± 0.09	0.91 ± 0.09	0.91 ± 0.05
pcaNNet	0.92 ± 0.07	0.96 ± 0.04	0.91 ± 0.15	0.92 ± 0.08	0.92 ± 0.08	0.92 ± 0.05
RF	0.87 ± 0.05	0.95 ± 0.05	0.87 ± 0.09	0.87 ± 0.08	0.86 ± 0.07	0.86 ± 0.05
svmRadial	0.93 ± 0.05	0.96 ± 0.03	0.87 ± 0.10	0.98 ± 0.04	0.98 ± 0.05	0.92 ± 0.07
NF vs. ICM
DT	0.75 ± 0.07	0.79 ± 0.09	0.76 ± 0.18	0.74 ± 0.13	0.76 ± 0.10	0.74 ± 0.11
ENet	0.80 ± 0.05	0.88 ± 0.07	0.79 ± 0.13	0.81 ± 0.13	0.83 ± 0.10	0.80 ± 0.06
pcaNNet	0.81 ± 0.08	0.88 ± 0.07	0.83 ± 0.11	0.78 ± 0.14	0.81 ± 0.10	0.81 ± 0.08
RF	0.82 ± 0.08	0.89 ± 0.07	0.78 ± 0.14	0.87 ± 0.09	0.86 ± 0.09	0.81 ± 0.10
svmRadial	0.69 ± 0.07	0.76 ± 0.08	0.61 ± 0.19	0.78 ± 0.17	0.76 ± 0.11	0.65 ± 0.11
DCM vs. ICM
DT	0.77 ± 0.07	0.79 ± 0.08	0.75 ± 0.14	0.78 ± 0.13	0.76 ± 0.11	0.75 ± 0.08
ENet	0.80 ± 0.06	0.85 ± 0.07	0.85 ± 0.12	0.76 ± 0.11	0.76 ± 0.07	0.80 ± 0.06
pcaNNet	0.78 ± 0.08	0.85 ± 0.07	0.80 ± 0.18	0.76 ± 0.13	0.76 ± 0.10	0.76 ± 0.10
RF	0.79 ± 0.08	0.89 ± 0.06	0.88 ± 0.11	0.72 ± 0.12	0.74 ± 0.09	0.80 ± 0.08
svmRadial	0.80 ± 0.07	0.90 ± 0.05	0.82 ± 0.11	0.78 ± 0.10	0.77 ± 0.09	0.79 ± 0.08

Values are presented as means ± standard deviation (total 50 iterations). AUC, area under the receiver operating characteristic curve; DCM, dilated cardiomyopathy; ICM, ischemic cardiomyopathy; NF, nonfailure controls; DT, decision tree; ENet, elastic net; pcaNNet, neural networks with principal component analysis; RF, random forest; svmRadial, support vector machine with radial kernel.