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. 2024 Sep 28;56(1):2409352. doi: 10.1080/07853890.2024.2409352

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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. — Developed and validated through a machine learning-integrated approach for constructing diagnostic models. (A) a comprehensive suite of 113 diagnostic models was meticulously evaluated for accuracy across all training and test datasets. Receiver operating characteristic curves and confusion matrices for the models were generated for (B) GSE150910, (C) GSE32537, (D) GSE53845, (E) GSE92592, and (F) GSE110147 datasets, illustrating the performance and predictive validity of each diagnostic model in distinguishing IPF cases from controls.