Graph-based adaptive node enhancement for Alzheimer’s disease prediction

Currently, there are issues of sample imbalance and insufficient sample quantity in graph-based Alzheimer's disease prediction methods. This can lead to classifiers being biased towards the majority class samples and result in overfitting. To address this problem, a graph-based data augmentation node expansion algorithm is proposed. Firstly, graph representation learning is used to reduce the original feature vectors to a low-dimensional space. This message aggregation method ensures that the low-dimensional vectors contain the potential structural information of the data, preventing structural damage that may arise from direct expansion on the original data. Secondly, in the low-dimensional space, an adaptive-weight node expansion algorithm is employed to generate new nodes, overcoming the boundary fuzziness issue of traditional oversampling algorithms. This weight expansion algorithm adjusts the priority of each expansion node to control the generation position and quantity of new nodes. Finally, the expanded graph is fed into a Graph Neural Network classifier for prediction. Quantitative experiments on the Tadpole dataset and NACC dataset demonstrate that the proposed graph-based data augmentation model achieves the highest accuracy. The average accuracy was 93.84% vs 92.8% on the Tadpole dataset and 90.11% vs 88.29% on the NACC dataset. In addition, additional ablation experiments have demonstrated the effectiveness of node expansion in graph structures.