Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis.

Cancer is caused by somatic mutations, a dreadful disease that impacts individuals everywhere. Classifying gene expression data is essential for disease diagnosis and distinguishing tumor types. However, small sample sizes, numerous features, and noise make this task particularly challenging. This is especially true when performing feature selection on high-dimensional microarray data. It is critical to select the most pertinent and valuable genes from microarray data to identify prospective biomarkers or gain insight into the fundamental mechanisms of cancer. This study introduces a novel hybrid model that combines feature selection and classification to identify the most significant and informative features from microarray data associated with brain cancer. The research employs the GSE50161 dataset obtained from the Curated Microarray Database (CuMiDa), comprising 130 samples classified into five distinct categories with 54,676 genomes examined. We first applied mRMR to reduce dimensionality by removing redundant features, followed by HHO to refine the feature subset for optimal classification performance. To improve the performance of our model in classifying brain cancer microarray data, we utilized three metaheuristic algorithms: Differential Evolution (DE), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO). The hyperparameters “C” and “sigma” of the support vector machine (SVM) were optimized using these algorithms. The experimental results indicate that the suggested framework improves the capacity to differentiate between benign and malignant tissues with reduced time and dimensionality requirements. Furthermore, the genes selected for the dataset on brain cancer have undergone biological interpretation. This process is consistent with the findings of relevant scientific inquiries and significantly influences patients’ prognoses.