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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, SVM, k-NN, and RF Models with NGAL and/or NT-proBNP: The Figure compares the ROC curves for the best performing models within each AI/ML technique with differing combinations that include NT-proBNP and/or NGAL. False positive rate (1 – specificity) and true positive rates (sensitivity) are reported on the x- and y-axis respectively. Panel A is for NGAL, NT-proBNP, plasma creatinine only. Panel B is for NGAL and UOP only. Panel C is for plasma creatinine, UOP, and NT-proBNP only. Panel D is for NT-proBNP, and UOP only.