Optimized Gene Selection Using Nomadic People and Salp Swarm Algorithms for Cancer Detection

Accurately detecting cancer through gene expression analysis is crucial for early diagnosis and effective treatment. However, gene expression data's high dimensionality and redundancy pose significant challenges, such as overfitting and computational inefficiency. To address these issues, we propose a hybrid feature selection framework that integrates a filter-wrapper approach with swarm intelligence for optimized gene selection. The proposed method utilizes the Nomadic People Optimizer (NPO) in conjunction with Mutual Information (MI) to identify a relevant subset of genes from high-dimensional datasets. An optimized Support Vector Machine (SVM) is employed to further enhance classification accuracy, with its hyperparameters fine-tuned using an enhanced Salp Swarm Algorithm (SSA) incorporating a crossover operator. This hybrid approach not only reduces the search space but also mitigates overfitting by leveraging the exploration and exploitation capabilities of the NPO and SSA. Experimental results on five cancer gene expression datasets-lung adenocarcinoma (LUAD), breast cancer (GSE2034), glioblastoma multiforme (GBM), ovarian cancer (GSE2109), and colorectal adenocarcinoma (COAD)-demonstrate that the proposed NPO-SSVM achieves classification accuracies ranging from 91.25% to 97.02% with AUC-ROC values between 0.85 and 0.97. The framework achieves an average feature reduction of 51% while outperforming state-of-the-art methods (GA, PSO, GWO, SSA) by 3-12% in classification accuracy and 15% in computational efficiency. These findings confirm that NPO-SSVM provides a robust and efficient solution for gene selection in cancer detection, offering significant advancements for personalized medicine and early diagnosis.