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. 2018 Oct 15;8:15229. doi: 10.1038/s41598-018-33449-0

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© The Author(s) 2018

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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Characterization of a Supported-Vector-Machine (SVM) prediction model for the identification of CLN2 based on 8 CSF-biomarkers. (a) Receiver-operating-characteristic (ROC)-analysis shows the sensitivity and specificity of the model with an area-under-the-curve (AUC) of 0.92 and (a) confidence interval (CI) of 0.667–1. (b) SVM-predicted class probabilities. A value of >0.5 leads to a CLN2 disease classification, a value of <0.5 to a classification as a control. (c) Results of the predictive classification of the SVM-model for the test and validation cohort. B12 and N4 are misclassified in the test cohort, B21 is misclassified in the validation cohort.