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Table 3: The performance of feature combinations with the SVM classifier in differentiating long- and short-term survival time
Feature Set N_F^{^a} SVM
Sensitivity Specificity Accuracy AUC
CM^{^b} 26 0.7910 0.6667 0.7293 0.7786
RLM 22 0.7313 0.4242 0.5789 0.5880
Histogram 8 0.6866 0.2576 0.4737 0.4326
CM + RLM^{^b} 48 0.7761 0.7727 0.7744 0.8053
CM + histogram 34 0.7761 0.6818 0.7293 0.7782
RLM + histogram 30 0.8060 0.3788 0.5940 0.6223
CM + RLM + histogram^{^c} 56 0.7761 0.7879 0.7820 0.8057
Note:—AUC indicates area under the curve.
↵a The number of features in each subset.
↵b The best classification when the feature type is used alone or pair-combined.
↵c The best classification result when all features are used.

Feature Set	N_F^{^a}	SVM
Feature Set	N_F^{^a}	Sensitivity	Specificity	Accuracy	AUC
CM^{^b}	26	0.7910	0.6667	0.7293	0.7786
RLM	22	0.7313	0.4242	0.5789	0.5880
Histogram	8	0.6866	0.2576	0.4737	0.4326
CM + RLM^{^b}	48	0.7761	0.7727	0.7744	0.8053
CM + histogram	34	0.7761	0.6818	0.7293	0.7782
RLM + histogram	30	0.8060	0.3788	0.5940	0.6223
CM + RLM + histogram^{^c}	56	0.7761	0.7879	0.7820	0.8057