Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Correction

Correction: Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis

  • The PLOS ONE Staff

Notice of republication

An incomplete, earlier version of this article was published in error. The publisher apologizes for the error. This article was republished on October 1, 2019 to correct for this error. Please download the 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.0223931.s001

(PDF)

S2 File. Republished corrected article.

https://doi.org/10.1371/journal.pone.0223931.s002

(PDF)

Reference

  1. 1. Suzuki S, Yamashita T, Sakama T, Arita T, Yagi N, Otsuka T, et al. (2019) Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PLoS ONE 14(9): e0221911. https://doi.org/10.1371/journal.pone.0221911 pmid:31499517

Citation: The PLOS ONE Staff (2019) Correction: Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PLoS ONE 14(10): e0223931. https://doi.org/10.1371/journal.pone.0223931

Published: October 10, 2019

Copyright: © 2019 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.