Summary:
- The article presents a novel machine learning-based framework for predicting the onset of Alzheimer's disease (AD) using multimodal neuroimaging data.
- The framework integrates structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) data to identify patterns that can distinguish between healthy individuals and those with AD.
- The proposed approach demonstrates high accuracy in predicting AD onset, highlighting the potential of multimodal neuroimaging and machine learning techniques for early detection and diagnosis of this neurodegenerative disorder.