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. 2022 Jan 10;12:328. doi: 10.1038/s41598-021-03687-w

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© The Author(s) 2022

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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Decision trees generated with 6 features at three sensitivities (95%, 90%, 85%). (a) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 95% sensitivity. (b) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 90% sensitivity. (c) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 85% sensitivity. TP true positive, TN true negative, FP false positive, FN false negative, PPV positive predictive value = TP/(TP + FP), NPV negative predictive value = TN/(TN + FN).

Decision trees generated with 6 features at three sensitivities (95%, 90%, 85%). (a) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 95% sensitivity. (b) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 90% sensitivity. (c) The decision tree built with the 6 features identified by the proposed DT-based method and delivering 85% sensitivity. TP true positive, TN true negative, FP false positive, FN false negative, PPV positive predictive value = TP/(TP + FP), NPV negative predictive value = TN/(TN + FN).