Medical fraud detection method based on weighted GraphSAGE and generative adversarial network

Medicare fraud analysis and detection is the most critical task in medical fund supervision, essential to ensure medical funds’ security and sustainable development. To ensure the accuracy of medicare fraud detection, one needs to explore the patient information in the data fully. However, many detection models have poor generalization ability and degraded performance when dealing with medicare imbalanced datasets that lack fraud samples. Therefore, this paper proposes a medicare fraud detection method based on weighted GraphSAGE and generative adversarial network. This method combines the representation of relationship features of patient visits with weighted GraphSAGE algorithm-based patient feature extraction and employs generative adversarial network to construct detection models. Experiments demonstrate that the proposed method significantly improves the recognition performance of the model. Meanwhile, we compare the proposed method with advanced mainstream recognition techniques such as meta-path vectors, convolutional neural network, graph attention network, heterogeneous graph attention network and one-class adversarial nets. The proposed recognition method performs better in Recall, Precision, F1-score and Accuracy. Moreover, its performance remains stable under different data sizes and various positive and negative sample ratios, offering better generalization. © 2024 Systems Engineering Society of China. All rights reserved.