Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.

ABSTRACT:

Background

With the increasing impact of tuberculosis on public health, accurately predicting future tuberculosis cases is crucial for optimizing of health resources and medical service allocation. This study applies a self-attention mechanism to predict the number of tuberculosis cases, aiming to evaluate its effectiveness in forecasting.

Methods

Monthly tuberculosis case data from Changde City between 2010 and 2021 were used to construct a self-attention model, a long short-term memory (LSTM) model, and an autoregressive integrated moving average (ARIMA) model. The performance of these models was evaluated using three metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

Results

The self-attention model outperf

SUBMITTER: Lv Z 

PROVIDER: S-EPMC11613505 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature