• Yavanaboina Tezaaw
• Dr K.Vijaya Lakshmi

Determining early-stage Brain tumor through gene expression data offers significant challenges for effective treatment. Gene expression datasets typically consist of a multitude of features, each representing specific genes. However, the presence of irrelevant or redundant features often leads to multicollinearity issues, complicating analysis and decision-making processes. To overcome these problems, this research proposes the Bootstrapping Guidance Strategy based on the Spider Wasp Optimization (BGS-SWO) approach for the feature selection of brain cancer gene expression. Initially, the two standard datasets as Curated Microarray Database (CuMiDa) and the Cancer Genome Atlas (TCGA) are used for the estimation of the proposed method.Pre-processing techniques like handling missing values and min-max normalization are performed to enhance the quality of the input data. Then, the BGS-SWO approach is used for the selection of important features and these are classified by proposing the Contextual Bayesian Optimization based Support Vector Machine (CBO-SVM) approach. The experimental results demonstrate that the proposed BGS-SWO with CBO-SVM approach attains a better accuracy of 99.35%, precision of 98.34%, recall of 99.10% and F1-score of 98.71% on CuMiDa dataset as compared to Depth-wise Separable Convolutional Neural Network (DSCNN) method.

Bootstrapping Guidance Strategy, Gene Expression Data , Contextual Bayesian Optimization, Spider Wasp Optimization, Support Vector Machine

"Feature Selection of Brain Tumor based on Gene Expression using Bootstrapping Spider Wasp Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.c179-c185, September-2025, Available :http://www.jetir.org/papers/JETIR2509223.pdf

"Feature Selection of Brain Tumor based on Gene Expression using Bootstrapping Spider Wasp Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppc179-c185, September-2025, Available at : http://www.jetir.org/papers/JETIR2509223.pdf