A comparative study of ARIMA and LSTM models in predicting hospital discharge number / 公共卫生与预防医学

ABSTRACT

Objective To fit and predict the monthly discharge number of a specialist hospital using Autoregressive Integrated Moving Average model (ARIMA) and Long Short-Term Memory Neural Network model (LSTM), and compare the prediction effects of the two models. Methods ARIMA and LSTM models were constructed based on the monthly discharge number of a specialist hospital from 2013 to 2018. The resulting models were then used to predict the monthly discharge numbers in 2019, which were compared with actual data. The mean absolute percentage error (MAPE) was used to evaluate the prediction effect of these two models. Results The MAPE values of ARIMA and LSTM compared to actual data in 2019 were 7.90% and 14.26%, respectively. Conclusion The prediction effect of ARIMA was better than that of LSTM. The prediction results of ARIMA showed that the number of patients discharged from the specialist hospital in 2019 was increasing, which fit well with the actual data.