Retraction
Noval Approach For Chronic Kidney Disease Using Machine Learning Methodology
Paper Information
Record ID:
35903
Author(s):
Journal:
Publication Date:
March 25, 2021
Retraction Date:
February 23, 2022
(3.7 years years ago)
Subjects:
Institutions:
Country:
🇮🇳 IndiaArticle Type:
Publisher:
IOP Publishing
Open Access:
Yes
PubMed ID:
Not indexed in PubMed
Retraction PubMed ID:
Not indexed in PubMed
Retraction Details
Retraction Reasons:
Nature of Retraction:
Retraction
Retraction Notice:
10.1088/1742-6596/1916/1/012402Citations (5)
5
Total Citations3
Post-Retraction(60.0%)
1
Pre-Retraction0
Same DayPost-Retraction Citation Analysis
0
Within 30 days
2
Within 1 year
0
After 2+ years
486
Days since retraction (latest)
Heavy Metals Removal Using Carbon Based Nanocomposites
Unknown Authors
Unknown Journal
Published: Unknown
Performance Evaluation and Comparative Analysis of Several Machine Learning Classification Techniques Using a Data-driven Approach in Predicting Renal Failure
R P Prawin
International Journal for Research in Applied Science and Engineering Technology
Open Access
Published: Jun 2023
1 citation
1 citation
486 days after retraction
A modified weighted mean of vectors optimizer for Chronic Kidney disease classification
Essam H. Houssein, Awny Sayed
Computers in Biology and Medicine
Published: Feb 2023
22 citations
22 citations
358 days after retraction
Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Nishin James, Jitendra Kaushik
2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)
Published: Apr 2022
1 citation
1 citation
64 days after retraction
Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives
Khalid Alhamazani, Jalawi Sulaiman Alshudukhi, Saud Aljaloud et al. (4 authors)
Computational Intelligence and Neuroscience
Open Access
Published: Jan 2021
12 citations
12 citations
418 days before retraction
Quick Stats
Total Citations:
7
Years Since Retraction:
3.7 years
Open Access:
Yes
Last Checked:
Jul 24, 2025