基于ARIMA与NNAR模型的中国食管癌疾病负担预测

马倩倩; 何贤英; 崔芳芳; 孙东旭; 翟运开; 高景宏; 王琳; 赵杰

doi:10.16462/j.cnki.zhjbkz.2021.09.010

基于ARIMA与NNAR模型的中国食管癌疾病负担预测

doi: 10.16462/j.cnki.zhjbkz.2021.09.010

1.
450052 郑州，郑州大学第一附属医院互联网医疗系统与应用国家工程实验室
2.
450001 郑州，郑州大学管理工程学院

基金项目:

国家超级计算郑州中心创新生态系统建设科技专项 201400210400

国家重点研发计划 2017YFC0909900

河南省高校科技创新团队支持计划 20IRTSTHN028

河南省医学科技攻关计划联合共建项目 LHGJ20200331

河南省医学科技攻关计划联合共建项目 2018020120

河南省青年科学基金项目 202300410409

详细信息

通讯作者:
赵杰，E-mail: zhaojie@zzu.edu.cn

中图分类号: R735.1;R195.1
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- 文章访问数: 468
- HTML全文浏览量: 310
- PDF下载量: 96
- 被引次数: 0
出版历程
- 收稿日期: 2021-02-01
- 修回日期: 2021-03-23
- 网络出版日期: 2021-10-23
- 刊出日期: 2021-09-10

Prediction of disease burden of esophageal cancer in China based on ARIMA and NNAR models

1.
National Engineering Laboratory for Internet Medical Systems and Applications, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
2.
School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds:

National Supercomputing Zhengzhou Center Innovation Ecosystem Construction Technology Project 201400210400

National Key Research and Development Program of China 2017YFC0909900

Innovation Research Team of Higher Education in Henan Province 20IRTSTHN028

the Joint Construction Project of the Henan Province Medical Science and Technology Research Plan LHGJ20200331

the Joint Construction Project of the Henan Province Medical Science and Technology Research Plan 2018020120

Henan Youth Science Fund Project 202300410409

More Information

Corresponding author: ZHAO Jie,E-mail: zhaojie@zzu.edu.cn

摘要

摘要: 目的探索中国食管癌疾病负担时间序列特征，并进行预测。方法收集1990—2019年中国食管癌发病率、死亡率、伤残调整寿命年(disability adjusted life year, DALY)等疾病负担数据，基于1990—2016年数据建立自回归移动平均(autoregressive integrated moving average, ARIMA)模型、神经网络自回归(neural network autoregression, NNAR)模型，通过平均误差率(modulation error ratio, MER)、平均绝对百分比误差(mean absolute percentage error, MAPE)、平均绝对误差(mean absolute error, MAE)和均方根误差(root mean squared error, RMSE)比较2017—2019年预测数据与实际数据以验证模型预测性能，并预测2020—2024年食管癌疾病负担。结果 1990—2019年中国食管癌疾病负担整体呈波动上升趋势，发病率上升33.26%，死亡率上升21.26%，DALY率上升6.66%。ARIMA模型与NNAR模型的预测值和实际值动态趋势基本一致，选择其中更优模型预测得到2020—2024年中国食管癌发病率分别为20.375/10万、21.057/10万、21.380/10万、21.341/10万和21.080/10万；死亡率分别为18.834/10万、19.647/10万、20.407/10万、20.889/10万和20.988/10万。DALY率分别为418.192/10万、431.123/10万、442.780/10万、452.376/10万和459.358/10万。结论中国食管癌疾病负担在2020—2024年将上升。NNAR模型在拟合中国食管癌疾病负担应用中具有良好预测性能与精度，可为疾病负担短期预测提供借鉴方法。
- 自回归移动平均模型 /
- 神经网络自回归模型 /
- 食管癌 /
- 预测 /
- 疾病负担
Abstract: Objective To explore the time series characteristics of the disease burden of esophageal cancer in China and predict the disease burden of esophageal cancer. Methods The incidence, mortality, and disability adjusted life year (DALY) of esophageal cancer in China from 1990 to 2019 were collected. autoregressive integrated moving average (ARIMA) model and neural network autoregression (NNAR) model were established based on the data from 1990 to 2016, and model prediction performance was verified by comparing 2017-2019 forecast data with actual data through mean absolute percentage error (MAPE), modulation error ratio (MER), mean absolute error (MAE) and root mean squared error (RMSE). The better model was applied to predict the disease burden of esophageal cancer from 2020 to 2024. Results From 1990 to 2019, the overall disease burden of esophageal cancer in China showed a fluctuating upward trend, with the incidence rate rising by 33.26%, the mortality rate rising by 21.26%, and the DALY rate rising by 6.66%. The predicted values of disease burden by ARIMA model and NNAR model were basically consistent with the actual dynamic trend. The incidence rate of esophageal cancer in China from 2020 to 2024 would be 20.375/100 000, 21.057/100 000, 21.380/100 000, 21.341/100 000, 21.080/100 000, and mortality rate would be 18.834/100 000, 19.647/100 000, 20.407/100 000, 20.889/100 000, 20.988/100 000, and the DALY rate would be 418.192/100 000, 431.123/100 000, 442.780/100 000, 452.376/100 000, and 459.358/100 000. Conclusions The disease burden of esophageal cancer in China will increase slightly from 2020 to 2024. The NNAR model demonstrates good prediction performance and accuracy in simulating the disease burden of esophageal cancer in China, and provides a reference method for short-term prediction of the disease burden.
- Autoregressive integrated moving average model /
- Neural network autoregression model /
- Esophageal cancer /
- Forecast /
- Disease burden

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图 1 NNAR模型结构

Figure 1. Structure of NNAR model

下载: 全尺寸图片幻灯片

图 2 1990—2019年中国食管癌疾病负担趋势

Figure 2. Trends in the disease burden of esophageal cancer in China from 1990 to 2019

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图 3 ARIMA(0, 2, 1)食管癌发病预测模型

Figure 3. ARIMA(0, 2, 1) esophageal cancer incidence prediction model

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图 4 NNAR(2, 2)食管癌发病预测模型

Figure 4. NNAR (2, 2) esophageal cancer incidence prediction model

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图 5 ARIMA(0, 2, 1)食管癌死亡模型

Figure 5. ARIMA(0, 2, 1) esophageal cancer death model

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图 6 NNAR(2, 2)食管癌死亡模型

Figure 6. NNAR(2, 2) esophageal cancer death prediction model

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图 7 ARIMA(2, 0, 0)食管癌DALYs预测模型

Figure 7. ARIMA(2, 0, 0) DALYs prediction model for esophageal cancer

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图 8 NNAR(3, 2)食管癌DALYs预测模型

Figure 8. NNAR(3, 2) DALYs prediction model for esophageal cancer

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表 1 中国食管癌疾病负担预测模型对比

Table 1. Comparison of prediction models of esophageal cancer disease burden in China

模型	训练集				测试集
模型	MAPE (%)	MER (%)	MAE	RMSE	MAPE (%)	MER (%)	MAE	RMSE
发病率
ARIMA	0.920	0.945	0.170	0.248	3.312	3.375	0.636	0.759
NNAR	0.587	0.598	0.109	0.136	2.760	2.810	0.529	0.625
死亡率
ARIMA	0.911	0.935	0.166	0.237	1.756	1.796	0.314	0.424
NNAR	0.596	0.611	0.109	0.140	0.404	0.411	0.072	0.097
DALY率
ARIMA	0.920	0.955	4.119	5.283	0.875	0.890	3.506	4.516
NNAR	0.551	0.562	2.456	3.287	1.070	1.090	4.291	5.649

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