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Ansari Shiri, Mohammad, Mansouri, Najme. (1404). Relevance-Based Sailfish Optimizer for Robust and Compact Breast Cancer Diagnosis Models. , 57(1), 157-184. doi: 10.22059/jac.2025.397665.1232
Mohammad Ansari Shiri; Najme Mansouri. "Relevance-Based Sailfish Optimizer for Robust and Compact Breast Cancer Diagnosis Models". , 57, 1, 1404, 157-184. doi: 10.22059/jac.2025.397665.1232
Ansari Shiri, Mohammad, Mansouri, Najme. (1404). 'Relevance-Based Sailfish Optimizer for Robust and Compact Breast Cancer Diagnosis Models', , 57(1), pp. 157-184. doi: 10.22059/jac.2025.397665.1232
Ansari Shiri, Mohammad, Mansouri, Najme. Relevance-Based Sailfish Optimizer for Robust and Compact Breast Cancer Diagnosis Models. , 1404; 57(1): 157-184. doi: 10.22059/jac.2025.397665.1232

Relevance-Based Sailfish Optimizer for Robust and Compact Breast Cancer Diagnosis Models

Journal of Algorithms and Computation
مقاله 11، دوره 57، شماره 1، آبان 2025، صفحه 157-184    اصل مقاله (3.07 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2025.397665.1232
نویسندگان
Mohammad Ansari Shiri1؛ Najme Mansouri* 2
1Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran
2Department of Computer science, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
Machine learning models for breast cancer diagnosis are often hindered by the high dimensionality of clinical datasets, where many features are redundant or irrelevant, degrading predictive performance. To address this challenge, this paper proposes a novel hybrid feature selection method, the Relevance-Based Sailfish Optimizer Feature Selection (RBSOFS), designed to identify a minimal yet highly informative subset of features. The RBSOFS approach was implemented and evaluated on the Breast Cancer Wisconsin (Diagnostic) dataset, with the selected features being fed into five established classifiers: Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR). The proposed method demonstrates superior performance compared to eight other state-of-the-art metaheuristic algorithms, achieving a peak accuracy of 98.24%. Significantly, this result was obtained using a subset of only 6-7 features, a drastic reduction that leads to simpler and more computationally efficient models compared to competing methods.
کلیدواژه‌ها
Healthcare؛ Relevance computation؛ Sailfish Optimization؛ Accuracy؛ Filter method
آمار
تعداد مشاهده مقاله: 72
تعداد دریافت فایل اصل مقاله: 57





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