Alzheimer’s Disease Risk Prediction and Pathogeny Extraction Using Fuzzy Graph Evolutionary Generative Adversarial Network

This research proposes FGE-GAN (fuzzy graph evolutionary generative adversarial network, a deep learning model that uses fuzzy graphs to handle uncertainty in disease data) to predict Alzheimer's disease risk and identify disease pathways. The model treats Alzheimer's progression as the spread of fuzzy entropy (uncertain information) through interconnected disease factors, and experiments show it outperforms existing methods at predicting disease risk.