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. 2024 Jun 12;15:5034. doi: 10.1038/s41467-024-49390-y

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — 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 CARPE_Coll. cannot be evaluated. The performance difference between random forest and CARPE_ECG 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.