Table 3.

Detailed results of machine learning-based classification of 5-year major adverse cardiovascular and cerebral events [95% confidence interval].

Machine learning classifier	Sensitivity %	Specificity %	Precision	Recall	F-Measure	AUC from ROC curve analysis	PRC Area
ANN	85.2 [82.9–87.6]	17.4 [15–20]	0.94 [0.9–0.97]	0.85 [0.83–0.88]	0.9 [0.86–0.94]	0.79 [0.74–0.84]	0.94 [0.93–0.96]
J48 (C4.5)	85.1 [82.5–87.6]	17.3 [15–20]	0.94 [0.9–0.98]	0.85 [0.82–0.88]	0.9 [0.85–0.94]	0.51 [0.48–0.53]	0.84 [0.81–0.86]
NaïveBayes	82.9 [80–86.2]	83.7 [75.7–92]	0.88 [0.83–0.92]	0.83 [0.8–0.86]	0.89 [0.86–0.91]	0.88 [0.83–0.92]	0.98 [0.97–0.99]
RandomForest	89.4 [87.4–91]	31.7 [23.3–40]	0.98 [0.96–1]	0.89 [0.87–0.91]	0.96 [0.94–0.98]	0.8 [0.74–0.86]	0.94 [0.92–0.96]
SMO	90 [88.4–91.6]	16.7 [16.7–16.7]	1 [1–1]	0.9 [0.88–0.92]	1 [1–1]	0.5 [0.5–0.5]	0.83 [0.81–0.86]

ANN Artificial neural network (multilayer perceptron), AUC area-under-the-curve, PRC precision recall curve, ROC receiver operator characteristics, SMO sequential minimal optimization.