Deep learning time series prediction models in surveillance data of hepatitis incidence in China.
Deep learning time series prediction models in surveillance data of hepatitis incidence in China.
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DOI：
10.1371/journal.pone.0265660
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Zhang, Xingyu
中科院分区：
文献类型：
--
作者：
Xia, Zhaohui;Qin, Lei;Ning, Zhen;Zhang, Xingyu
肝炎传染病的准确发病率预测对于早期预防和更好的政府战略规划至关重要。在本文中，我们根据中国大陆国家公共卫生监测系统的肝炎月发病率，使用深度学习方法提出了不同的预测模型。我们评估并比较了三种深度学习方法的性能，即长短期记忆（LSTM）预测模型、循环神经网络（RNN）预测模型和反向传播神经网络（BPNN）预测模型。 2005年至2018年收集的数据用于训练和预测模型，同时通过5折交叉验证对数据进行分割。性能根据三个指标进行评估：均方误差 (MSE)、平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE)。 2005年至2018年，该系统监测了20,924,951例病例和11,892例死亡。乙型肝炎（HB）是导致发病和死亡的最主要疾病，所占比例大于70%，而2018年的发病和死亡比例与2005年相比有很大下降。基于测量误差和三个神经网络的可视化，没有一种模型可以完全优于其他模型来预测发病病例。在预测乙型肝炎发病数时，三个模型的性能排名从高到低依次为LSTM、BPNN、RNN，而丙型肝炎（HC）则为LSTM、RNN、BPNN。而 HB、HC 的 LSTM 模型的 MAE、MSE 和 MAPE 分别为 3.84*10−06、3.08*10−11、4.981、8.84*10−06、1.98*10−12,5.8519。深度学习时间序列预测模型显示了其对肝炎发病率预测的重要性，并有可能帮助决策者做出有效决策，及早发现疾病事件，这将显着促进肝炎疾病的控制和管理。
Precise incidence prediction of Hepatitis infectious disease is critical for early prevention and better government strategic planning. In this paper, we presented different prediction models using deep learning methods based on the monthly incidence of Hepatitis through a national public health surveillance system in China mainland. We assessed and compared the performance of three deep learning methods, namely, Long Short-Term Memory (LSTM) prediction model, Recurrent Neural Network (RNN) prediction model, and Back Propagation Neural Network (BPNN) prediction model. The data collected from 2005 to 2018 were used for the training and prediction model, while the data are split via 5-Fold cross-validation. The performance was evaluated based on three metrics: mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Among the year 2005–2018, 20,924,951 cases and 11,892 deaths were supervised in the system. Hepatitis B (HB) is the most disease-causing incidence and death, and the proportion is greater than 70 percent, while the percentage of the incidence and deaths is decreased much in 2018 compared with 2005. Based on the measured errors and the visualization of the three neural networks, there is no one model predicting the incidence cases that can be completely superior to other models. When predicting the number of incidence cases for HB, the performance ranking of the three models from high to low is LSTM, BPNN, RNN, while it is LSTM, RNN, BPNN for Hepatitis C (HC). while the MAE, MSE and MAPE of the LSTM model for HB, HC are 3.84*10−06, 3.08*10−11, 4.981, 8.84*10−06, 1.98*10−12,5.8519, respectively. The deep learning time series predictive models show their significance to forecast the Hepatitis incidence and have the potential to assist the decision-makers in making efficient decisions for the early detection of the disease incidents, which would significantly promote Hepatitis disease control and management.
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作者：
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