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. 2021 Jun 16;11:12686. doi: 10.1038/s41598-021-92155-6

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© The Author(s) 2021

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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Effect of confounding variables. This figure shows that of all the confounders evaluated, largest amount of deviance in the classifications is explained by age. Also, predictions by machine learning explain a large portion of the deviance that is not explained by the confounder variables. Htn hypertension, BMI body mass index, BSA body surface area, DAFIT—synthetic data creation using main and side study data; DAFIT Filt2—combining DAFIT with feature filtering from ICC2; DAFIT Filt3—combining DAFIT with feature filtering from ICC3.