Open AccessPredicting the Cases of Hepatitis B with the A-LSTM Model
Author(s) -
Yang Li,
Yali Yang,
Cong Yang,
Baolin Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1995/1/012007
Subject(s) - artificial neural network , hepatitis , hepatitis b , hepatitis b virus , mean squared error , medicine , statistics , value (mathematics) , artificial intelligence , mathematics , computer science , virology , virus
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.