Table 4.

Performance measures of different ML algorithms for classifications of different types of cardiomyopathies and subtype recognition using only selected HCGs

	Accuracy	AUC	Sensitivity	Specificity	Precision	F1
NF vs. DCM
DT	0.85 ± 0.06	0.85 ± 0.06	0.77 ± 0.10	0.92 ± 0.06	0.90 ± 0.08	0.82 ± 0.07
ENet	0.86 ± 0.06	0.93 ± 0.05	0.83 ± 0.10	0.88 ± 0.07	0.86 ± 0.08	0.84 ± 0.07
pcaNNet	0.83 ± 0.05	0.90 ± 0.05	0.83 ± 0.09	0.83 ± 0.09	0.82 ± 0.08	0.82 ± 0.05
RF	0.90 ± 0.06	0.96 ± 0.03	0.87 ± 0.08	0.93 ± 0.08	0.92 ± 0.08	0.89 ± 0.06
svmRadial	0.84 ± 0.05	0.93 ± 0.04	0.78 ± 0.09	0.89 ± 0.07	0.87 ± 0.07	0.82 ± 0.06
NF vs. ICM
DT	0.77 ± 0.08	0.79 ± 0.08	0.78 ± 0.17	0.76 ± 0.13	0.78 ± 0.10	0.76 ± 0.11
ENet	0.85 ± 0.06	0.95 ± 0.05	0.80 ± 0.11	0.91 ± 0.07	0.91 ± 0.07	0.84 ± 0.07
pcaNNet	0.90 ± 0.06	0.97 ± 0.04	0.89 ± 0.10	0.90 ± 0.09	0.91 ± 0.08	0.89 ± 0.06
RF	0.84 ± 0.06	0.92 ± 0.06	0.82 ± 0.11	0.86 ± 0.09	0.86 ± 0.08	0.84 ± 0.07
svmRadial	0.74 ± 0.09	0.85 ± 0.06	0.65 ± 0.18	0.84 ± 0.14	0.82 ± 0.12	0.71 ± 0.12
DCM vs. ICM
DT	0.78 ± 0.07	0.78 ± 0.10	0.75 ± 0.15	0.81 ± 0.13	0.79 ± 0.11	0.75 ± 0.09
ENet	0.82 ± 0.06	0.88 ± 0.07	0.86 ± 0.11	0.79 ± 0.12	0.79 ± 0.09	0.82 ± 0.07
pcaNNet	0.85 ± 0.06	0.90 ± 0.07	0.84 ± 0.10	0.85 ± 0.09	0.84 ± 0.09	0.83 ± 0.07
RF	0.85 ± 0.07	0.94 ± 0.04	0.91 ± 0.10	0.79 ± 0.14	0.80 ± 0.10	0.85 ± 0.07
svmRadial	0.81 ± 0.08	0.91 ± 0.06	0.79 ± 0.13	0.82 ± 0.10	0.79 ± 0.10	0.79 ± 0.09

Values are presented as means ± standard deviation (total 50 iterations). HCG, highly contributing gene; ML, machine learning; 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.