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. 2020 Jan 14;10:205. doi: 10.1038/s41598-019-57083-6

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© The Author(s) 2020

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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ROC Curve Analysis for Optimized DNN, LR, k-NN, SVM, and RF Models with Traditional AKI Biomarkers: The Figure compares the average ROC curves for the best performing models within each ML method with differing combinations that include UOP and/or creatinine. False positive rate (1 – specificity) and true positive rates (sensitivity) are reported on the x- and y-axis respectively. Panel A is for plasma creatinine and UOP only. Panel B is for plasma creatinine only, and Panel C shows UOP only. Area under the ROC curve values are reported in the bottom right of each Panel.