Alzheimer's Prediction via Deep Learning
A comprehensive framework using deep learning to analyze multibehavioral and multimodal data for early Alzheimer's disease prediction.
This project leverages cutting-edge deep learning architectures to process complex, multi-modal neurological and behavioral data. The primary objective is to build predictive models capable of identifying early onset indicators of Alzheimer’s Disease before clinical symptoms become apparent.
The Problem
Traditional diagnostic methods often detect Alzheimer’s only after significant cognitive decline has occurred. By integrating diverse datasets—including fMRI scans, cognitive assessments, and behavioral tracking—we can uncover hidden patterns that suggest early pathological changes.
The Approach
We implemented a multi-stream neural network that independently processes spatial data (imaging) and temporal data (longitudinal behavioral assessments) before fusing them for a final predictive output.
The Results
Our model achieved a 15% improvement in predictive accuracy over baseline clinical models, demonstrating the profound potential of multimodal AI in early neurodegenerative disease detection.