宁波市老年人总死亡风险的预测模型比较研究

黄钰姝; 宋和佳; 张睿; 何江; 程义斌; 李永红; 姚孝元

doi:10.13421/j.cnki.hjwsxzz.2022.11.005

宁波市老年人总死亡风险的预测模型比较研究

Predictive models for overall mortality risk in the elderly population in Ningbo, China: a comparative analysis

摘要

摘要:
目的探讨比较多变量长短期记忆神经网络（LSTM）与多元自回归移动平均模型（ARIMAX）在宁波市老年人总死亡人数预测中的效果。
方法收集2014年1月1日— 2018年12月31日宁波市老年人总死亡人数、气象因素及空气污染物数据。以2014年1月1日— 2018年2月28日的周数据为训练集建立多变量LSTM及ARIMAX模型，以2018年3月1日— 2018年12月31日周数据为测试集预测周死亡数，并根据均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、拟合优度（R²）等预测误差评价指标及预测曲线图比较两模型预测效果。
结果本研究共收集2014年1月1日— 2018年12月31日宁波市老年人死亡人数146 606人，平均每周死亡人数的中位数为76人。周平均最高温度、相对湿度、降水量分别为23.37℃、80.53%、0.86 mm，周平均SO₂、NO₂、CO、O₃-8h、PM₁₀、PM_2.5质量浓度分别为11.71 μg/m³、37.43 μg/m³、0.79 mg/m³、95.43 μg/m³、58.43 μg/m³、35.93 μg/m³。通过气象因素与空气污染物的Spearman秩相关分析，最终将最高温度、相对湿度、降水量、O₃-8h及PM_2.5五个因素纳入分析。通过多变量LSTM和ARIMAX（3，1，2）模型进行预测，两模型的预测误差评价指标RMSE、MAE、MAPE和R²值分别为4.90、3.77、4.77、0.82和8.68、5.80、7.53、0.97，ARIMAX的曲线拟合度优于多变量LSTM模型。
结论对于2018年3月1日— 2018年12月31日宁波市老年人周死亡人数预测，ARIMAX模型的预测能力优于多变量LSTM。

Abstract:
Objective To compare the effects of the multivariate long short term memory neural network (LSTM) model versus the autoregressive integrated moving average model-X (ARIMAX) model in predicting total deaths among the elderly population in Ningbo, China.
Methods The total number of deaths among the elderly population, meteorological data, and air pollutant data were collected in Ningbo from January 1, 2014 to December 31, 2018. The multivariate LSTM and ARIMAX models were established with the weekly data from January 1, 2014 to February 28, 2018 as the training set and the weekly data from March 1 to December 31, 2018 as the test set for prediction. The predictive effects of the two models were compared based on predictive curves and error assessment indices such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), goodness of fit (R²).
Results In this study, a total of 146 606 deaths were collected in Ningbo from January 1, 2014 to December 31, 2018, with a median of 76 deaths per week. The weekly mean maximum temperature, relative humidity, and precipitation were 23.37℃, 80.53%, and 0.86 mm, respectively, and the weekly mean mass concentrations of SO₂, NO₂, CO, O₃-8 h, PM₁₀, and PM_2.5 were 11.71 μg/m³, 37.43 μg/m³, 0.79 mg/m³, 95.43 μg/m³, 58.43 μg/m³, and 35.93 μg/m³, respectively. Based on the Spearman rank correlation analysis of meteorological factors and air pollutants, five factors were finally included in analysis, i.e., maximum temperature, relative humidity, precipitation, O₃-8 h, and PM_2.5. The multivariable LSTM and ARIMAX (3, 1, 2) models were used for prediction, and the multivariate LSTM model had an RMSE of 4.90, an MAE of 3.77, an MAPE of 4.77, and an R² of 0.82, while the ARIMAX (3, 1, 2) model had an RMSE of 8.68, an MAE of 5.80, an MAPE of 7.53, and an R² of 0.97. The ARIMAX had a higher degree of curve fitting than the LSTM model.
Conclusion Multivariate ARIMAX has better predictive ability than multivariate LSTM for the weekly mean number of deaths among the elderly population in Ningbo from March 1 to December 31, 2018.

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