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. 2021 Jul 7;11:14007. doi: 10.1038/s41598-021-93087-x

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© The Author(s) 2021

Open AccessThis 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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Predictive accuracies (AUC-ROC) and the 95% confidence intervals across the student and general population samples, using three different classifiers and four different feature sets. (A) Shows the results of logistic regression to predict depression from different data sources in college students (light blue) and the general population sample (dark blue). It also highlights the most predictive features in the mobility model for the student sample. Red (green) indicates a negative (positive) relationship with depression. (B) shows the corresponding results using random forest and XGBoost algorithms. Data collected using the MindDoc app (https://minddoc.de/app).