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. 2024 Jan 11;14:1084. doi: 10.1038/s41598-023-47934-8

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© The Author(s) 2024

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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Feature weights of support vector machines (SVM) with the linear kernel. To assess the decision-making of SVM to differentiate subjects with major depressive disorder (MDD) from healthy controls (HC), we investigate the importance of the structural brain features by looking at the corresponding feature weights for the regional cortical surface areas, cortical thicknesses and subcortical volumes. The horizontal bars indicate the 95% confidence interval calculated using percentile method via bootstrapping.