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

backgroundFatty 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.

methodsWe 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 was conducted to identify clusters with distinct risk factors.

resultsOur findings indicate that attention-based GNNs significantly outperformed conventional models in predicting fatty liver disease under semi-supervised settings, with statistically significant improvements in area under the curves (AUCs) (all

conclusionAttention-based GNNs demonstrated strong predictive performance for fatty liver disease using a small number of labeled samples. This methodological approach illustrates how graph-based learning can leverage relational structures in routine clinical data to support data-efficient, individualized risk assessment in label-constrained settings.