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JCSIS

Swarm Intelligence-Optimized Support Vector Machine for Breast Cancer Diagnosis in Imbalanced Clinical Datasets.

Sam M. K.
Volume 5

Abstract

Breast cancer remains the most prevalent malignancy among women globally, accounting for over 2.3 million new diagnoses annually, making early and accurate diagnosis a decisive factor in reducing mortality and improving therapeutic outcomes. Machine learning-based diagnostic systems, particularly Support Vector Machines (SVMs), have demonstrated considerable promise in automated tumor classification; however, their performance is critically hindered by two persistent challenges: the sensitivity of SVM hyperparameters to suboptimal manual tuning, and the pervasive class imbalance inherent in real-world clinical datasets, where malignant cases represent a significantly smaller proportion than benign instances. This paper proposes a novel Swarm Intelligence-Optimized Support Vector Machine (SI-SVM) framework that addresses both challenges simultaneously through an integrated pipeline combining advanced metaheuristic optimization with robust class-balancing strategies for accurate breast cancer diagnosis. Specifically, a hybrid optimization mechanism fusing Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) is employed to simultaneously perform feature selection, SVM kernel selection, and hyperparameter tuning — including regularization parameter C and kernel coefficient γ — within a unified search space, thereby circumventing the computational burden of exhaustive grid search. To mitigate the class imbalance problem, a combination of Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) — collectively referred to as SMOTEEN — was applied during preprocessing to generate a balanced and denoised training distribution that preserves the statistical integrity of minority class samples. The proposed framework was rigorously evaluated on three benchmark clinical datasets: the Wisconsin Breast Cancer Diagnostic Dataset (WBCD), the SEER Breast Cancer Dataset, and the MIAS mammographic image dataset, collectively encompassing over 100,000 clinical records with diverse feature representations including cytological characteristics, hormonal biomarkers, and imaging descriptors. A stratified 10-fold cross-validation protocol was adopted to ensure unbiased performance estimation across all experimental configurations. Experimental results demonstrate that the proposed SI-SVM framework achieves a classification accuracy of 99.1%, a sensitivity of 98.7%, a specificity of 99.4%, an F1-score of 0.989, and an AUC-ROC of 0.997 on the WBCD dataset — significantly outperforming standalone SVM, PSO-SVM, GWO-SVM, Random Forest, and XGBoost baselines. Furthermore, SHAP-based feature importance analysis identified the most diagnostically discriminative clinical features — including bare nuclei, clump thickness, and uniformity of cell shape — providing transparent and actionable insights aligned with established oncological knowledge. The proposed framework offers a computationally efficient, generalizable, and clinically interpretable solution for automated breast cancer screening, with strong potential for integration into computer-aided diagnosis (CAD) systems in resource-constrained medical environments.

Keywords

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