Improved chaos-enhanced FOX for clustering-based supervised medical classification

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.