Academic Journal
Improved chaos-enhanced FOX for clustering-based supervised medical classification.
| Τίτλος: | Improved chaos-enhanced FOX for clustering-based supervised medical classification. |
|---|---|
| Συγγραφείς: | Dağlı İ; Department of Computer Engineering, Institute of Science, Selçuk University, Konya, Turkey. idagli@erbakan.edu.tr., İnan O; Department of Computer Engineering, Institute of Science, Selçuk University, Konya, Turkey., Başçiftçi F; Department of Computer Engineering, Institute of Science, Selçuk University, Konya, Turkey. |
| Πηγή: | Scientific reports [Sci Rep] 2026 May 06; Vol. 16 (1). Date of Electronic Publication: 2026 May 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Ιατρικοί όροι (MeSH): | Medical Informatics*/methods , Classification Algorithms* , Clustering Algorithms*, Humans ; Algorithms ; Cluster Analysis ; Nonlinear Dynamics |
| Περίληψη: | Despite the widespread use of optimization-based classification methods in medical data analysis, many existing approaches suffer from premature convergence and limited robustness when dealing with complex and heterogeneous datasets. To address these limitations, this study presents a chaos-enhanced, fox-inspired classification framework derived from the Fox Optimization Algorithm. The proposed method employs a Gauss/Mouse chaotic map to regulate the exploration-exploitation balance through the control variable, while preserving the original algorithmic structure without introducing additional parameters. The framework adopts a clustering-based classification strategy in which cluster centers are optimized using the proposed method, and class labels are assigned via distance-based nearest-neighbor analysis. The approach was evaluated on six publicly available medical datasets, including Breast Cancer Wisconsin Diagnostic, Breast Cancer Wisconsin Original, Dermatology, Thyroid, Hepatitis, and Heart, using accuracy, precision, sensitivity, and specificity as evaluation metrics. Experimental results demonstrate that the proposed framework achieves statistically significant and consistent classification performance, attaining the best overall average rank (1.16) in the Friedman test (p = 0.0012) and outperforming several baseline methods. Performance improvements over benchmark methods were observed across multiple datasets, while comparable results were obtained on others. The incorporation of chaotic dynamics effectively enhances search behavior by mitigating premature convergence. Statistical analyses, including the Friedman test, further confirm the significance of the observed improvements. Overall, the findings indicate that the proposed framework provides stable and reproducible classification performance across benchmark medical datasets. Future studies may extend this work through external clinical validation and alternative methodological integrations. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Ethical approval: The authors confirm that the necessary ethical review process has been adhered to in this study. Competing interests: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: Chaotic maps; Classification; Clustering; Fox optimization algorithm; Optimization |
| Entry Date(s): | Date Created: 20260506 Date Completed: 20260705 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13334007 |
| DOI: | 10.1038/s41598-026-50872-w |
| PMID: | 42091957 |
| Βάση Δεδομένων: | MEDLINE |
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