Enrichment of machine learning classifiers performance in detection of leukaemia from microarray genes through bio-inspired feature extraction methods – an overview

Early and accurate detection of leukaemia is essential for improving treatment outcomes; however, achieving high diagnostic reliability from high-dimensional microarray gene data remains challenging. This study investigates the application of optimised machine learning frameworks for leukaemia classification through systematic feature selection and classifier optimisation. Metaheuristic algorithms—whale optimisation algorithm, Grey Wolf Optimisation, Particle Swarm Optimisation and Flower Pollination Algorithm —were employed for gene subset extraction, followed by feature selection using Invasive Weed Optimisationand Harmonic Search. Multiple classifiers, including K-Nearest Neighbors, Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine with linear, polynomial and RBF kernels, were evaluated using Accuracy, F1 score, MCC, Error Rate, Critical Success Index (CSI), G-mean and Cohen's Kappa. Among all the evaluated combinations, the PSO–HS–SVM (RBF) framework achieved superior performance. The findings demonstrate that the synergistic integration of optimisation-based feature selection with nonlinear SVM classifiers substantially enhances classification performance. These results highlight the potential of optimised machine learning approaches for reliable leukaemia detection while emphasising that the conclusions are limited to computational validation.