Table 4. Performance of the machine-learning (ML) algorithms predicting 10-year cardiovascular disease (CVD) risk derived from applying training algorithms on the validation cohort of 82,989 patients.

Higher c-statistics results in better algorithm discrimination. The baseline (BL) ACC/AHA 10-year risk prediction algorithm is provided for comparative purposes.

Algorithms	AUC c-statistic	Standard Error*	95% Confidence Interval		Absolute Change from Baseline
			LCL	UCL
BL: ACC/AHA	0.728	0.002	0.723	0.735	—
ML: Random Forest	0.745	0.003	0.739	0.750	+1.7%
ML: Logistic Regression	0.760	0.003	0.755	0.766	+3.2%
ML: Gradient Boosting Machines	0.761	0.002	0.755	0.766	+3.3%
ML: Neural Networks	0.764	0.002	0.759	0.769	+3.6%

*Standard error estimated by jack-knife procedure [30]