基于长短时记忆神经网络的手足口病发病趋势预测

Incidence trend prediction of hand-foot-mouth disease based on long short-term memory neural network

针对传统手足口病(HFMD)发病趋势预测算法预测精度不高、未结合其他影响因素、预测时间较短等问题,提出结合气象因素使用长短时记忆(LSTM)网络进行长期预测的方法。首先,将发病序列通过滑动窗口的方式转化为网络的输入和输出;然后采用LSTM网络进行数据建模和预测,并使用迭代预测的方式获得较长期的预测结果;最后在网络中增加温度和湿度变量,比较这些变量对预测结果的影响。实验结果表明,加入气象因素能够提高模型的预测精度,所提模型在济南市数据集上的平均绝对误差(MAE)为74.9,在广州市数据集上的MAE为427.7,相较于常用的季节性差分自回归移动平均(SARIMA)模型和支持向量回归(SVR)模型,该模型的预测准确率更高。可见所提模型是HFMD发病趋势预测的一种有效的实验方法。

In order to solve the problems of the traditional Hand-Foot-Mouth Disease(HFMD)incidence trend prediction algorithm,such as low prediction accuracy,lack of the combination of other influencing factors and short prediction time,a method of long-term prediction using meteorological factors and Long Short-Term Memory(LSTM)network was proposed.First,the sliding window was used to convert the incidence sequence into the input and output of the network.Then,the LSTM network was used for data modeling and prediction,and the iterative prediction was used to obtain the long-term prediction results.Finally,the temperature and humidity variables were added to the network to compare the impact of these variables on the prediction results.Experimental results show that adding meteorological factors can improve the prediction accuracy of the model.The proposed model has the Mean Absolute Error(MAE)on the Jinan dataset of 74.9,and the MAE on the Guangzhou dataset of 427.7.Compared with the commonly used Seasonal Autoregressive Integrated Moving Average(SARIMA)model and Support Vector Regression(SVR)model,the proposed model has the prediction accuracy higher,which proves that the model is an effective experimental method for the prediction of the incidence trend of HFMD.