Relevant gene selection using ANOVA-ant colony optimisation approach for malaria vector data classification

Recent progress in gene expression data research makes it possible to quantify and identify several thousand genes' expressions simultaneously. For malaria infection and transmission, gene expression data classification using dimensionality reduction is a standard approach in gene expression data analysis and proposed for this study. A major problem occurs in the reduction of high dimensional data, it plays a significant role in improving the precision of classification, allowing biologists and clinicians to correctly predict infections in humans by choosing a limited subclass of appropriate genes and deleting redundant, and noisy genes. The combination of a novel analysis of variance (ANOVA) with ant colony optimisation (ACO) approach as a hybrid feature selection to select relevant genes is suggested in this study to minimise the redundancy between genes, and SVM is used for classification. The proposed method's efficacy was shown by the experimental outcomes based on the high-dimensional of gene expression data.