Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.

ABSTRACT:

Background

Fatty liver disease is a common condition linked to metabolic syndrome, cardiovascular diseases, and liver cirrhosis, and timely, accurate diagnosis is crucial. In clinical studies, incorporating deep learning models often faces the challenge of scarce labeled data. This study investigates the effectiveness of graph-based deep learning models with attention mechanisms to predict fatty liver disease even with limited labeled data.

Methods

We utilized a dataset of 7,953 individuals, focusing on clinical variables obtained during health check-ups. Graph Neural Networks (GNNs) with attention mechanisms were assessed for predicting fatty liver disease in a semi-supervised learning setting. GNNExplainer was employed for feature importance analysis, and subgroup analysis

SUBMITTER: Kim SY 

PROVIDER: S-EPMC12815896 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature