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Table 5 Mean testing accuracy and the AUC of ensemble learning methods after 50 replicates with standard deviation in the brackets

From: Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

Model Accuracy AUC
Two-step stacking (14 models) \(\mathbf {87.7}\% \mathbf {(0.023)}\) 0.904 (0.026)
Two-step stacking (3 models) \(79.4\% (0.028)\) 0.822 (0.030)
Traditional stacking (14 models) \(81.8\% (0.033)\) 0.854 (0.034)
Traditional stacking (3 models) \(76.7\% (0.038)\) 0.798 (0.037)
Weighted voting (14 models) \(73.3\% (0.033)\) 0.751 (0.040)
Weighted voting (3 models) \(71.7\% (0.035)\) 0.728 (0.037)
Two-step stacking with GLPS only \(63.3\% (0.034)\) 0.674 (0.047)
  1. Best results are bolded