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. 2023 Jul 31;21:517. doi: 10.1186/s12967-023-04387-x

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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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Importance of features in ML models and subgroup analysis of BCPMD features. a Importance rank of all features for identifying CIAKI in diabetes included in the 6 different ML models. The size of the circles represents the degree of contribution to CIAKI. The different colors of circles represent different models. b A subset of (a), showed the degree of contributions of 15 features in BCPMD for identifying CIAKI relative to all features of (a) in 6 different models. c Degree of contributions of 15 features in BCPMD for identifying CIAKI relative to each other in 6 different models. d Subgroup analysis of BCPMD features. Violin plots show the distribution of continuous features included in BCPMD between CIAKI patients (n = 634) and non-CIAKI patients (n = 2880). The thick black line in the middle represents the interquartile range [IQR]. The thin black line represents the 95% confidence interval. The white point is the median, and the shapes on both sides represent the distribution density of the data. The median [IQR] of the features shown in Fig. d are listed in Additional file 1: Table S5. Bar plots show the incidence of CIAKI among the categorical features of BCPMD. BCPMD, brief CIAKI prediction model for diabetes based on the XGBT model