Incidence trend and prediction of hepatitis C based on stacked LSTM

Abstract

Objective To explore the prediction of hepatitis C incidence by stacked LSTM model. Methods Aiming at the incidence trend and the number of cases of hepatitis C in China from 2007 to 2017, the ARIMA, NNAR, SVR and stacked LSTM were used to train them. The model was used to predict the incidence of hepatitis C in the second half and the last quarter of 2017, and compared with the actual values. The prediction effects of the four models were compared and analyzed using the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). Results The SVR model performs not well. However, ARIMA model, NNAR model and stacked LSTM model can identify the incidence trend of hepatitis C in China from 2007 to 2017, and the RMSE values of the ARIMA model and the NNAR model are larger, and these two are relatively similar. On the contrary, the RMSE value of the stacked LSTM model is smaller. On the whole, compared with ARIMA model and NNAR model, it decreases by at least 20 percentage. The predicted MAPE value of the stacked LSTM model is less than 1%, meanwhile it is lower than the value of ARIMA or NNAR models. Conclusion The stacked LSTM model has the best predictive effect on the incidence of hepatitis C.

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