ARIMA、GM(1,1) 和 LSTM 模型在中国结核病病例预测中的研究。
The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.
机构信息
Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China.
Department of Medical Administration, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan, P.R. China.
出版信息
PLoS One. 2022 Feb 23;17(2):e0262734. doi: 10.1371/journal.pone.0262734. eCollection 2022.
BACKGROUND AND OBJECTIVE
Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China.
METHODS
The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People's Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy.
RESULTS
There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models.
CONCLUSIONS
Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.
背景与目的
结核病（Tuberculosis，TB）是中国的一个公共卫生问题，不仅危害人口健康，还影响经济和社会发展。需要进行准确的预测分析，以帮助决策者进行预警，并提供有效的预防措施。本研究分别构建并比较了 ARIMA、GM(1,1)和 LSTM 模型。结果表明，LSTM 是最优模型，可对中国大陆的结核病病例进行预测，达到令人满意的效果。
方法
从中华人民共和国国家卫生健康委员会网站提取中国大陆结核病病例数据。根据 TB 数据特征和样本要求，创建了 ARIMA、GM(1,1)和 LSTM 模型，可以对 TB 流行趋势进行预测。采用平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）评估模型拟合预测精度的效果。
结果
中国大陆 2018 年 1 月至 2020 年 12 月共有 3021995 例结核病病例，总体呈下降趋势。分别建立了 ARIMA、GM(1,1)和 LSTM 模型。最优的 ARIMA 模型是 ARIMA（0,1,0）×（0,1,0）12。GM(1,1)模型的方程为 X(k+1)=-10057053.55e(-0.01k)+10153178.55，均方差比 C 值为 0.49，小概率误差 P 值为 0.94。LSTM 模型由输入层、隐藏层和输出层组成，参数 epoch 和 learning rate 分别为 60 和 0.01。LSTM 模型的 MAE、RMSE 和 MAPE 值均小于 GM(1,1)和 ARIMA 模型。
结论
研究结果表明，LSTM 模型是最优模型，其精度性能优于 ARIMA 和 GM(1,1)模型。其预测结果可作为中国大陆结核病预防措施的预测工具。
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