Improving breast cancer classification with mRMR + SS0 + WSVM: a hybrid approach

Detecting breast cancer through histopathological images is time-consuming due to their volume and complexity. Speeding up early detection is crucial for timely medical intervention. Accurately classifying microarray data faces challenges from its dimensionality and noise. Researchers use gene selection techniques to address this issue. Additional techniques like pre-processing, ensemble, and normalization procedures aim to improve image quality. These can also impact classification approaches, helping resolve overfitting and data balance issues. A more sophisticated version could potentially boost classification accuracy while reducing overfitting. Recent technological advances have driven automated breast cancer diagnosis. This research introduces a novel method using Salp Swarm Optimization (SSO) and Support Vector Machines (SVMs) for gene selection and breast tumor classification. The process involves two stages: mRMR preselects genes based on their relevance and distinctiveness, followed by SSO-integrated WSVM for classification. WSVM, aided by SSO, trims redundant genes and assigns weights, enhancing gene significance. SSO also fine-tunes kernel parameters based on gene weights. Experimental results showcase the effectiveness of the mRMR-SSO-WSVM method, achieving high accuracy, precision, recall, and F1-score on breast gene expression datasets. Specifically, our approach achieved an accuracy of 99.62%, precision of 100%, recall of 100%, and an F1-score of 99.10%. Comparative analysis with existing methods demonstrates the superiority of our approach, with a 4% improvement in accuracy and a 3.5% increase in F1-score over traditional SVM-based methods. In conclusion, this study demonstrates the potential of the proposed mRMR-SSO-WSVM methodology in advancing breast cancer classification, offering significant improvements in performance metrics and effectively addressing challenges such as overfitting and data imbalance.