Table 3.

Analytical performance of machine learning with random optimization in the testing set.

Performance Parameter	ML-RO-0	ML-RO-4	DSS Model a
F-measure b	0.696	0.677	0.698
Accuracy	0.853	0.838	0.860
Area under the curve (AUC)	0.822	0.813	0.815
(+)LR (95% CI)	9.1 (4.3–20.8)	8.5 (3.9–19.6)	8.6 (4.2–18.0)
(−)LR (95% CI)	0.4 (0.3–0.6)	0.4 (0.3–0.6)	0.4 (0.2–0.5)
HR (95% CI)	10.7 (4.6–24.8)	10.3 (4.5–23.7)	10.9 (4.5–26.6)

LR: Likelihood ratio; C.I.: Confidence interval; HR: Hazard ratio; ^a Analytical performance was evaluated after categorization 0/1 based on risk estimate achieved by both predictors; ^b F-measure represents a harmonic mean of precision [(P) positive predictive value in machine learning] and recall [(R) sensitivity in machine learning] and is calculated as: 2PR/(P+R).