AI Article Synopsis
- The study focuses on predicting tuberculosis cases to enhance public health resource allocation, using a self-attention mechanism for improved accuracy.
- Monthly case data from 2010 to 2021 in Changde City was used to compare the performance of three models: self-attention, LSTM, and ARIMA, evaluated on RMSE, MAE, and MAPE metrics.
- Results showed the self-attention model significantly outperformed the others, demonstrating its effectiveness in accurately forecasting tuberculosis cases, which can aid health departments in better resource management.
Article 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 outperformed the other models in terms of prediction accuracy. On the test set, the RMSE of the self-attention model was approximately 7.41% lower than that of the LSTM model, MAE was reduced by about 10.99%, and MAPE was reduced by approximately 9.87%. Compared to the ARIMA model, RMSE was reduced by about 28.86%, MAE by about 32.22%, and MAPE by approximately 29.89%.
Conclusion: The self-attention model can effectively improve the prediction accuracy of tuberculosis cases, providing guidance for health departments optimizing of health resources and medical service allocation.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11613505 | PMC |
| http://dx.doi.org/10.1186/s12879-024-10183-9 | DOI Listing |
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