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Open Access

Correction

Correction: The efficacy of machine learning models in lung cancer risk prediction with explainability

  • The PLOS ONE Staff

Notice of Republication

This article was republished on August 27, 2024, to correct an error in the affiliation 6, which was introduced during the typesetting process. The publisher apologizes for the error. Please download this article again to view the correct version. The originally published, uncorrected article and the republished, corrected article are provided here for reference.

Supporting information

S1 File. Originally published, uncorrected article.

https://doi.org/10.1371/journal.pone.0310604.s001

(PDF)

S2 File. Republished, corrected article.

https://doi.org/10.1371/journal.pone.0310604.s002

(PDF)

Reference

  1. 1. Pathan RK, Shorna IJ, Hossain MS, Khandaker MU, Almohammed HI, Hamd ZY (2024) The efficacy of machine learning models in lung cancer risk prediction with explainability. PLoS ONE 19(6): e0305035. pmid:38870229

Citation: The PLOS ONE Staff (2024) Correction: The efficacy of machine learning models in lung cancer risk prediction with explainability. PLoS ONE 19(9): e0310604. https://doi.org/10.1371/journal.pone.0310604

Published: September 12, 2024

Copyright: © 2024 The PLOS ONE Staff . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.