In order to improve the hospital financial information management ability,an anomaly detection model combining Principal Component Analysis(PCA)and Variational Auto-Encoders(VAE)is constructed.Based on collection and preprocessing financial data,feature extraction by PCA,learning the potential distribution of data using VAE and improving the predictive performance of the model by k-fold cross-validation.The experimental results showed that PCA-VAE showed excellent performance in the anomaly detection task with the training set ratio of 9∶1,with its accuracy,recall and F1 scores of 0.946 7,0.942 1 and 0.944 4,respectively,significantly outperforming traditional machine learning algorithms and classification models combining PCA methods.
维普
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