Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia worldwide, affecting approximately 50 million people with projections exceeding 150 million by 2050. Early detection of AD is critically important as interventions initiated during the prodromal phase—particularly mild cognitive impairment—have the greatest potential to slow disease progression and preserve cognitive function. Machine learning, particularly ensemble methods like Random Forest, has emerged as a powerful tool for early AD prediction by analysing complex, multimodal data including neuroimaging, genetic markers, cognitive assessments, and fluid biomarkers. This paper provides a comprehensive review of Random Forest applications in AD prediction, synthesizing findings from recent studies.The combination of Backward Elimination Feature Selection with Artificial Ant Colony Optimization has achieved 95% accuracy while reducing computation time by 81%. Key challenges including class imbalance, model interpretability, and cross-cohort generalizability are addressed through techniques such as SMOTE and SHAP analysis. This review provides researchers and clinicians with a comprehensive understanding of Random Forest's role in early AD prediction and identifies promising directions for future research.

Keywords: Alzheimer's disease, machine learning, Random Forest, early prediction, ensemble learning, feature selection, biomarker analysis, neuroimaging, cognitive assessment

Downloads: | DOI: 10.17148/IARJSET.2026.13361