Expression of Concern: Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2
Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Healthcare · Machine Learning and Data Classification · Anomaly Detection Techniques and Applications
After this article [1] was published, the following concerns were noted:
In light of the cumulative issues, the PLOS One Editors issue this Expression of Concern.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1Talari P, Bharathiraja N, Kaur G, Alshahrani H, Al Reshan MS, Sulaiman A, et al. Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2. P Lo S ONE. 2024;19(1):e 0292100. doi: 10.1371/journal.pone.0292100 38236900 PMC 10796060 · doi ↗ · pubmed ↗
- 2Marappan R, Vardhini PAH, Kaur G, Murugesan S, Kathiravan M, Bharathiraja N, et al. Retracted article: Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks. Soft Comput. 2023;27(16):11853–67. doi: 10.1007/s 00500-023-08623-w · doi ↗
- 3Marappan R, Vardhini PAH, Kaur G, Murugesan S, Kathiravan M, Bharathiraja N, et al. Retraction Note: Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks. Soft Comput. 2024;28(S 1):371–371. doi: 10.1007/s 00500-024-10146-x · doi ↗
- 4Kamel Rahimi A, Canfell OJ, Chan W, Sly B, Pole JD, Sullivan C, et al. Machine learning models for diabetes management in acute care using electronic medical records: A systematic review. Int J Med Inform. 2022;162:104758. doi: 10.1016/j.ijmedinf.2022.104758 35398812 · doi ↗ · pubmed ↗
- 5UCI Machine Learning, Kaggle Team. Pima Indians Diabetes Database. Kaggle. Available from: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database