Expression of Concern: Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices

Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsArtificial Intelligence in Healthcare · Biomedical Text Mining and Ontologies · ECG Monitoring and Analysis
After this article [1] was published, the following concerns were noted:
The authors stated that the retracted Reference 13 was included in the manuscript before it was retracted, and the retraction was not identified prior to publication of [1]. They requested that this reference be removed from [1]. The Editors recognize that the cited reference does not impact the reliability of the study.
The authors asserted that the References 35, and 41–43, which are all co-authored by authors of [1], are relevant to the methodological framework of this study and demonstrate their research group’s expertise in similar research topics.
In light of the cumulative issues and concerns, which were not resolved in our discussions with the authors, the PLOS One Editors issue this Expression of Concern.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Kumar A, Dhanka S, Sharma A, Sharma A, Maini S, Fahlevi M, et al. Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices. P Lo S One. 2025;20(6):e 0325900. doi: 10.1371/journal.pone.0325900 40531844 PMC 12176113 · doi ↗ · pubmed ↗
