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CORRECTION article

Front. Artif. Intell., 02 February 2024
Sec. Medicine and Public Health
Volume 7 - 2024 | https://doi.org/10.3389/frai.2024.1373254

Corrigendum: Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

This article is a correction to:

  1. Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

    1. Read original article
\r\nNitasha KhanNitasha Khan1Muhammad Amir RazaMuhammad Amir Raza2Nayyar Hussain MirjatNayyar Hussain Mirjat3Neelam BalouchNeelam Balouch4Ghulam AbbasGhulam Abbas5Amr Yousef,Amr Yousef6,7Ezzeddine Touti Ezzeddine Touti8*
  • 1Department of Electrical Engineering, Nazeer Hussain University, Karachi, Pakistan
  • 2Department of Electrical Engineering, Mehran University of Engineering and Technology, Khairpur Mirs, Sindh, Pakistan
  • 3Department of Electrical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan
  • 4Department of Zoology, Shah Abdul Latif University Khairpur Mirs, Khairpur Mirs, Pakistan
  • 5School of Electrical Engineering, Southeast University, Nanjing, China
  • 6Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia
  • 7Engineering Mathematics Department, Alexandria University, Alexandria, Egypt
  • 8Department of Electrical Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia

A corrigendum on
Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease

by Khan, N., Raza, M. A., Mirjat, N. H., Balouch, N., Abbas, G., Yousef, A., and Touti, E. (2024). Front. Artif. Intell. 6:1339988. doi: 10.3389/frai.2023.1339988

In the published article, there was an error in the Acknowledgments, which listed an incorrect project number (“NBU-FFR-2023-0177”).

The correct Acknowledgments appear below.

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2024-2448-05”.

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: forecasting, public health, medicine, deep learning, machine learning

Citation: Khan N, Raza MA, Mirjat NH, Balouch N, Abbas G, Yousef A and Touti E (2024) Corrigendum: Unveiling the predictive power: a comprehensive study of machine learning model for anticipating chronic kidney disease. Front. Artif. Intell. 7:1373254. doi: 10.3389/frai.2024.1373254

Received: 19 January 2024; Accepted: 22 January 2024;
Published: 02 February 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2024 Khan, Raza, Mirjat, Balouch, Abbas, Yousef and Touti. 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: Ezzeddine Touti, esseddine.touti@nbu.edu.sa

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.