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. 2021 Jan 4;4:3. doi: 10.1038/s41746-020-00372-6

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© The Author(s) 2021

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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Fig. 2 — SHapley Additive exPlanations (SHAP) beeswarm plot for predicting COVID-19 diagnosis, showing SHAP values for the most important features of the model. Features in the summary plots (y-axis) are organized by their mean absolute SHAP values. Each point corresponds to an individual person in the study. The position of each point on the x-axis shows the impact that feature has on the classifier’s prediction for a given individual. Values of those features (i.e., fever) are represented by their color.