Predicting the Cases of Hepatitis B with the A-LSTM Model. Issue 1 (August 2021)

• Record Type: Journal Article
• Authors: Li, Yang; Yang, Yali; Yang, Cong; Zhang, Baolin
• Is Part Of: Journal of physics. Volume 1995:Issue 1(2021)
• Publication Date: 2021-08
• Subjects: A-LSTM -- Hepatitis B -- Attention mechanism -- Predictive modelling; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1995/1/012007
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Hepatitis B is a disease caused by hepatitis B virus. It's of great value to predict the cases of hepatitis B because of its strong infectivity and carcinogenicity. To predict the monthly new patients of hepatitis B in China accurately, a neural network with an attention-based LSTM model is proposed. Driven by the historical data provided by the Data-center of China Public Health Science, the model's evaluation indexes of RMSE, MAPE, MAE and R-squared are 1780.495, 1.789%, 1469.208 and 0.867 respectively, while the evaluation indexes of BPNN are 3532.959, 3.311%, 2677.009 and 0.478 respectively. The result shows that A-LSTM model in this work has an excellent prediction on the monthly new patients of hepatitis B and performs much better than BPNN and other traditional time series models.