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

被引:0
作者
Lv, Zhihong [1 ]
Sun, Rui [1 ]
Liu, Xin [1 ]
Wang, Shuo [2 ]
Guo, Xiaowei [1 ]
Lv, Yuan [1 ]
Yao, Min [3 ]
Zhou, Junhua [1 ]
机构
[1] Hunan Normal Univ, Sch Med, Key Lab Mol Epidemiol Hunan Prov, Changsha 410013, Hunan, Peoples R China
[2] Changsha Univ Sci & Technol, Changsha 410114, Hunan, Peoples R China
[3] Hunan Prov Ctr Dis Control & Prevent, Changsha 410005, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Tuberculosis; Time series forecasting; Self-attention mechanism; ARIMA model; LSTM model;
D O I
10.1186/s12879-024-10183-9
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
BackgroundWith 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.MethodsMonthly 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).ResultsThe 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%.ConclusionThe 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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页数:13
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