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. 2023 Jun 1;13:8874. doi: 10.1038/s41598-023-35649-9

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© The Author(s) 2023, corrected publication 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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(a) Naive Bayes, (b) Random Forest, (c) XGBoost. Confusion matrices of the models, cancer specific survival. The Naive Bayes model (a) had the worse performance, besides not having balanced accuracy in both classes. On the other hand, the Random Forest (b) and XGBoost (c) models had a performance with a very similar accuracy.