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. 2019 Nov 13;9:16738. doi: 10.1038/s41598-019-52899-8

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© The Author(s) 2019

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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Modelling results after inclusion of metadata. (A) Random forest achieved the highest balanced accuracy on average (~0.55). (B) Both protein and metadata features ranked by their relative importance for RandomForest predictive performance. The last step was to evaluate if there is any clear separation between patients within individual types of endometriosis and controls with the inclusion of metadata. Results also showed that there is no improvement of discriminating performance of the classifiers if we look into individual type of endometriosis.