Breast cancer is one of the most prevalent diseases among women worldwide, and achieving early and accurate diagnosis is crucial for effective treatment and improved survival rates. The aim of this study is to enhance diagnostic performance by developing a hybrid classification model that integrates a fuzzy twin support vector machine based on dissimilarity measure (DFTSVM) with two metaheuristic optimization algorithms: particle swarm optimization (PSO) and salp swarm algorithm (SSA). The proposed models, referred to as PSO-DFTSVM and SSA-DFTSVM, are designed to optimize the parameters of DFTSVM in order to improve classification accuracy and robustness. Experimental results demonstrate that the PSO-DFTSVM model achieves superior accuracy compared to the SSA-DFTSVM model, underscoring its potential as an efficient and reliable tool for medical diagnosis.

Keywords