Table 2.

Diagnostic accuracy using different classification systems. Feature selection had a significant impact on classifier performance with Friedman test ${\chi }^2=35.3$, 5 d.f., $p=1.3\times {10}^{-6}$. V: Wilcoxon signed rank statistic of performance compared to complete system; p: Associated Bonferroni-corrected p-value.

	Sensitivity	Specificity	DSC	Accuracy	V	p
SVM-based CAD system
LOSO	86% ±3.48	94% ± 2.39	84% ± 3.49	90% ± 2.01	136	0.0024
Tenfold	78% ± 9.19	97% ± 4.83	85.96% ± 6.06	87.50% ± 4.86
Fourfold	85% ± 1.41	92% ± 5.65	88.11% ± 1.76	88.50% ± 2.20
Twofold	83% ±3.82	91% ± 3.83	86.44% ± 1.29	87% ± 1.15
Random forest-based CAD system
LOSO	76% ±4.29	96% ± 1.97	75% ± 4.27	86% ± 2.37	118	0.0054
Tenfold	74% ± 1.26	98% ± 4.21	83.61% ± 9.15	86% ± 7.37
Fourfold	71% ± 8.28	98% ± 2.31	81.87% ± 5.05	84.50% ± 3.41
Twofold	71% ±4.24	99% ± 1.41	80.87% ± 2.14	80.30% ± 1.41
Naive Bayes-based CAD system
LOSO	84% ±3.68	94% ± 2.38	82.33% ± 3.68	89% ± 2.19	136	0.0024
Tenfold	80% ± 1.05	97% ± 4.83	87.13% ± 7.10	88.50% ± 5.79
Fourfold	77% ± 6.00	97% ± 2.00	85.46% ± 4.36	87% ± 3.46
Twofold	77% ±4.24	95% ± 1.41	84.58% ± 2.03	86% ± 1.41
KNN-based CAD system
LOSO	80% ±4.02	99% ± 1.00	79.66% ± 4.01	89.50% ± 2.04	127.5	0.0114
Tenfold	75% ± 8.87	100% ± 0.00	85.49% ± 5.88	87.50% ± 4.43
Fourfold	71% ± 1.10	100% ± 0.00	82.61% ± 7.43	85.50% ± 5.50
Twofold	70% ±0.00	100% ± 0.00	82.35% ± 0.00	85% ± 0.00
Decision Trees-based CAD system
LOSO	80% ±4.02	99% ± 1.00	79.66% ± 4.01	89.50% ± 2.04	127.5	0.0114
Tenfold	75% ± 8.87	100% ± 0.00	85.49% ± 5.88	87.50% ± 4.43
Fourfold	71% ± 1.10	100% ± 0.00	82.61% ± 7.43	85.50% ± 5.50
Twofold	70% ±0.00	100% ± 0.00	82.35% ± 0.00	85% ± 0.00