Comparative Evaluation of Machine-Learning Classifiers for High-Dimensional Gene Expression Data

High-dimensional gene expression data pose substantial challenges for machine-learning–based diagnostic modelling due to extreme dimensionality, noise, heterogeneous measurement conditions, and limited sample sizes. These factors strongly affect classifier behaviour and necessitate systematic comparative evaluation under controlled experimental conditions. This study presents a comprehensive assessment of widely used machine-learning classifiers applied to high-dimensional transcriptomic data, with a focus on robustness, stability, and suitability for gene expression–driven decision-support systems. The analysis covers representative model families, including tree-based and ensemble methods, margin-based classifiers, and regularized linear models. All classifiers were evaluated within a unified analytical pipeline incorporating normalization, variance-based filtering, stratified data partitioning, Bayesian hyperparameter optimization, and repeated validation. Performance was assessed using accuracy, precision, recall, F1-score, and balanced accuracy to account for class imbalance and heterogeneous sample structures. Experimental results reveal pronounced differences in classifier performance under high-dimensional conditions. Ensemble-based approaches, particularly Random Forest and gradient boosting models, consistently achieved higher stability and balanced accuracy across heterogeneous datasets. Regularized linear models with elastic-net penalties performed competitively after dimensionality reduction, while support vector machines demonstrated high accuracy but required careful kernel and regularization tuning. In contrast, probabilistic classifiers exhibited increased sensitivity to noise and distributional heterogeneity. Overall, the findings emphasize the importance of ensemble learning and regularization strategies for reliable classification of transcriptomic data and provide a reproducible methodological framework for information-technology–driven diagnostic modelling based on gene expression profiles.