A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China.

ABSTRACT:

Background

Tuberculosis (TB) is the respiratory infectious disease with the highest incidence in China. We aim to design a series of forecasting models and find the factors that affect the incidence of TB, thereby improving the accuracy of the incidence prediction.

Results

In this paper, we developed a new interpretable prediction system based on the multivariate multi-step Long Short-Term Memory (LSTM) model and SHapley Additive exPlanation (SHAP) method. Four accuracy measures are introduced into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric Mean Absolute Percentage Error. The Autoregressive Integrated Moving Average (ARIMA) model and seasonal ARIMA model are established. The multi-step ARIMA-LSTM model is proposed f

SUBMITTER: Yang E 

PROVIDER: S-EPMC9128107 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature