Abstract
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, necessitating advanced diagnostic tools to improve early detection and personalized targeted therapy. This review synthesizes evidence on explainable ensemble learning approaches for HCC classification, emphasizing their integration with clinical workflows and multi-omics data. A systematic analysis [including datasets such as The Cancer Genome Atlas, Gene Expression Omnibus, and the Surveillance, Epidemiology, and End Results (SEER) datasets] revealed that explainable ensemble learning models achieve high diagnostic accuracy by combining clinical features, serum biomarkers such as alpha-fetoprotein, imaging features such as computed tomography and magnetic resonance imaging, and genomic data. For instance, SHapley Additive exPlanations (SHAP)-based random forests trained on NCBI GSE14520 microarray data (n = 445) achieved 96.53% accuracy, while stacking ensembles applied to the SEER program data (n = 1897) demonstrated an area under the receiver operating characteristic curve of 0.779 for mortality prediction. Despite promising results, challenges persist, including the computational costs of SHAP and local interpretable model-agnostic explanations analyses (e.g., TreeSHAP requiring distributed computing for metabolomics datasets) and dataset biases (e.g., SEER's Western population dominance limiting generalizability). Future research must address inter-cohort heterogeneity, standardize explainability metrics, and prioritize lightweight surrogate models for resource-limited settings. This review presents the potential of explainable ensemble learning frameworks to bridge the gap between predictive accuracy and clinical interpretability, though rigorous validation in independent, multi-center cohorts is critical for real-world deployment.