Skip to main content

CORRECTION article

Front. Public Health, 29 October 2021
Sec. Life-Course Epidemiology and Social Inequalities in Health
Volume 9 - 2021 | https://doi.org/10.3389/fpubh.2021.778749
This article is part of the Research Topic Application of Biostatistics and Epidemiological Methods for Cancer Research in Sub-Saharan Africa View all 5 articles

Corrigendum: Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study

This article is a correction to:

  1. Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study

    1. Read original article
\nOkechinyere J. Achilonu Okechinyere J. Achilonu1*June Fabian,June Fabian2,3Brendan Bebington,Brendan Bebington3,4Elvira Singh,Elvira Singh1,5Gideon Nimako,Gideon Nimako1,6M. J. C. EijkemansM. J. C. Eijkemans7Eustasius MusengeEustasius Musenge1
  • 1Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa
  • 2Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
  • 3Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
  • 4Department of Surgery, Faculty of Health Science University of the Witwatersrand Faculty of Science, Parktown, Johannesburg, South Africa
  • 5National Cancer Registry, National Health Laboratory Service, 1 Modderfontein Road, Sandringham, Johannesburg, South Africa
  • 6Industrialization, Science, Technology and Innovation Hub, African Union Development Agency (AUDA-NEPAD), Johannesburg, South Africa
  • 7Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands

A Corrigendum on
Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study

by Achilonu, O. J., Fabian, J., Bebington, B., Singh, E., Eijkemans, M. J. C., and Musenge, E. (2021). Front. Public Health 9:694306. doi: 10.3389/fpubh.2021.694306

In the published article, Gideon Nimako was not included as an author in the published article. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: colorectal, cancer, recurrence, survival, machine learning, filter feature selection, prediction

Citation: Achilonu OJ, Fabian J, Bebington B, Singh E, Nimako G, Eijkemans MJC and Musenge E (2021) Corrigendum: Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study. Front. Public Health 9:778749. doi: 10.3389/fpubh.2021.778749

Received: 17 September 2021; Accepted: 20 September 2021;
Published: 29 October 2021.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2021 Achilonu, Fabian, Bebington, Singh, Nimako, Eijkemans and Musenge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Okechinyere J. Achilonu, achilonu.okechinyere@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.