Enhancing Alzheimer's disease prediction using random forest: A novel framework combining backward feature elimination and ant colony optimization
Current Research in Translational Medicine2025Vol. 73(4), pp. 103526–103526
Citations Over TimeTop 10% of 2025 papers
Afeez A Soladoye, Nicholas Aderinto, Bolaji A Omodunbi, Adebimpe Esan, Ibrahim Adeyanju, David B. Olawade
Abstract
The integration of advanced feature selection with nature-inspired hyperparameter optimization enhances Alzheimer's disease prediction accuracy while improving computational efficiency. However, external validation on independent datasets and prospective clinical studies are needed to establish real-world utility. This methodological framework offers promising applications for early diagnosis and intervention planning, with potential extensions to other complex medical prediction tasks.
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