Alzheimer's disease classification by supervised and intelligent techniques.

Background: Significant advancements in neuroimaging have emerged over the past decade, notably through positron emission tomography (PET) and magnetic resonance imaging (MRI) for diagnosing Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Combining imaging modalities with machine learning (ML) techniques enhances diagnostic accuracy.

Objective: To develop predictive models using pre-treatment brain imaging data to distinguish between normal controls (NC), MCI, and AD stages, improving diagnostic precision.