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. 2020 Sep 19;19:105. doi: 10.1186/s12937-020-00611-2

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© The Author(s) 2020

Open AccessThis 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 — Leading dietary determinants for changes in cardiometabolic risk scores in children. This figure shows the contribution of the total variance in percentage by leading dietary determinants (selected from 26 food groups). Machine learning models including general linear regression model, random forest, and gradient boost machine were used to analyze the importance of dietary predictors for CMRS. Random forest had the highest prediction performance and this figure shows the leading dietary determinants derived from the random forest