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. 2024 Apr 8;56(1):2337738. doi: 10.1080/07853890.2024.2337738

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© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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Figure 4. — Machine learning and nomogram A, The correlation heatmap displays the correlations between various continuous variables. (*P<0.05, **P<0.01, ***P<0.001). B-C, Variable selection using LASSO regression. D-E, Variable selection using a random forest model to identify the top ten important variables. F, The Venn diagram illustrates the intersection between the results of LASSO regression and the random forest model. G, Nomogram model used to differentiate IVO and NVO. H, ROC curve of the nomogram model. I, Calibration curve of the nomogram model.