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

Abstract

Timely risk prediction of Alzheimer's disease (AD) holds significant clinical value. However, the inherent fuzziness of disease information hinders the deeper understanding of AD pathogenesis and limits the effectiveness of current predictive models. This article explores the staged evolutionary patterns of AD by integrating fuzzy graph-based disease modeling and deep learning. First, we use fuzzy graphs to quantify interpathogeny associations through fuzzy memberships. Second, we propose a fuzzy entropy propagation model to mathematically describe AD deterioration as the spread of fuzzy entropy information in fuzzy graphs. Finally, we introduce a novel fuzzy graph evolutionary generative adversarial network (FGE-GAN) for disease risk prediction and pathogeny extraction. In the generator of FGE-GAN, fuzzy graph convolution (FGC) layers are designed based on the mathematical model to capture AD's evolutionary patterns with interpretability. Experiments on multiple brain disease datasets indicate that FGE-GAN outperforms state-of-the-art methods in disease risk prediction. In addition, the extracted multiomics pathogenies provide valuable insights for early intervention. The code is available at: github.com/fmri123456/FGE-GAN.