Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.

ABSTRACT: Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline, with no known cure. Early detection of dementia, a primary manifestation of Alzheimer's disease, is critical to enable timely intervention and treatment planning. This study introduces ensemble learning models for predicting Alzheimer's disease and presents a comparative analysis between traditional machine learning and advanced ensemble models. The evaluation is conducted using the "Open Access Series of Imaging Studies" 2 (OASIS-2) dataset. Traditional models, including logistic regression, decision tree, support vector machine, and random forest, are benchmarked against ensemble models such as adaptive boosting, extreme gradient boosting, and a hyperparameter-tuned majorit

SUBMITTER: Amr Y 

PROVIDER: S-EPMC12923517 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature