Fig. 5: Diagnostic performance over age groups.
From: Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning
On the x-axes, we show different age groups in the held-out test and external validation set. Left y-axes: area under the receiver operator characteristic (AUROC). Error bars indicate 95% confidence intervals around the mean. Right y-axes: percentage of patients who comprise the respective subgroup of the x-axis. No cardiologist’s judgement is available in the external validation set, hence CARPEColl. cannot be evaluated. The performance difference between random forest and CARPEECG is strongest in the external validation set due to the conventional ML model relying (too) strongly on the “age” variable. Error bars indicate 95% confidence intervals over all models of all five splits. The number of individuals in each bin are 53, 143, 219, 248, 140 for the held-out test set and 281, 341, 208, 86, respectively. Source data are provided as a Source Data file.
