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. 2023 Sep 14;13:15213. doi: 10.1038/s41598-023-42542-y

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© The Author(s) 2023

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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The SHAP summary plot from the XGB classifier shows the ranking of the top-most contributing features. The positive contribution towards that class is shown on the positive side of the X-axis (representing positive SHAP values), while the negative side of the axis represents a negative contribution or the contribution of those features against the prediction of that class. The XGBoost model, data samples and running examples in the Python programming language are available through the GitHub platform (https://github.com/kasiakaczmarek/predicting-late-recurrence-of-atrial-fibrillation).