Towards a Classification and Detection Model for Medical Diagnosis Based on Machine Learning Algorithms

Abstract

High-throughput DNA microarray technology produces high-dimensional gene ex- pression data characterized by a large number of irrelevant and redundant genes, which exacerbates the curse of dimensionality and negatively impacts disease clas- sification performance. Consequently, effective feature selection is a critical step in gene expression analysis. This thesis investigates bio-inspired and deep learning–based feature selection strategies for gene expression data to improve classification performance through comparative analyses between single-objective and multi-objective Differential Evo- lution frameworks, Differential Evolution and autoencoder-based feature learning approaches, and Differential Evolution and the Slime Mould Algorithm, highlighting the benefits of multi-objective optimization in balancing the mean squared residual (MSR) and feature subset compactness; moreover, a novel clustering-based multi- objective Differential Evolution framework (CBDE) is proposed to enhance search efficiency and identify compact and biologically meaningful gene subsets. The proposed methods are evaluated on multiple benchmark microarray datasets using state-of-the-art classifiers, including Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR). Experimental results demonstrate that the applica- tion of feature selection significantly improves convergence behavior and classification performance, while promoting a diverse set of optimal solutions. Moreover, the proposed approaches consistently achieve effective dimensionality reduction and robust performance across different datasets.