Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, which can progress to more severe liver diseases such as liver fibrosis, cirrhosis and hepatocellular carcinoma. Approximately 50%-70% of T2DM patients also have NAFLD. Traditional diagnostic methods like liver biopsy have limitations, making large-scale screening difficult. In the past decade, machine learning have emerged as crucial tools for assisting in NAFLD diagnosis. In this paper, we propose a novel Dual Graph Attention Network (DGAN) for diagnosing NAFLD in T2DM patients. We model the NAFLD diagnosis problem as a node classification task on graph by using features similarity constructed graph. The model includes a Feature Attention Module to capture feature importance through a feature graph and a Patient Attention Module to evaluate patient importance using graph attention mechanisms. These components enhance the model's classification accuracy by leveraging both feature and topological information. The model was trained and tested on clinical data from 2402 T2DM patients, demonstrating superior accuracy in identifying NAFLD compared to other models.