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

MA Qian-qian; HE Xian-ying; CUI Fang-fang; SUN Dong-xun; ZHAI Yun-kai; GAO Jing-hong; WANG Lin; ZHAO Jie

doi:10.16462/j.cnki.zhjbkz.2021.09.010

Volume 25 Issue 9

Oct. 2021

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2021 > 25(9): 1048-1053

MA Qian-qian, HE Xian-ying, CUI Fang-fang, SUN Dong-xun, ZHAI Yun-kai, GAO Jing-hong, WANG Lin, ZHAO Jie. Prediction of disease burden of esophageal cancer in China based on ARIMA and NNAR models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(9): 1048-1053. doi: 10.16462/j.cnki.zhjbkz.2021.09.010

Citation:

MA Qian-qian, HE Xian-ying, CUI Fang-fang, SUN Dong-xun, ZHAI Yun-kai, GAO Jing-hong, WANG Lin, ZHAO Jie. Prediction of disease burden of esophageal cancer in China based on ARIMA and NNAR models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(9): 1048-1053. doi: 10.16462/j.cnki.zhjbkz.2021.09.010

Citation:

MA Qian-qian, HE Xian-ying, CUI Fang-fang, SUN Dong-xun, ZHAI Yun-kai, GAO Jing-hong, WANG Lin, ZHAO Jie. Prediction of disease burden of esophageal cancer in China based on ARIMA and NNAR models[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2021, 25(9): 1048-1053. doi: 10.16462/j.cnki.zhjbkz.2021.09.010

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

doi: 10.16462/j.cnki.zhjbkz.2021.09.010

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
Received Date: 2021-02-01
Rev Recd Date: 2021-03-23

Available Online: 2021-10-23

Publish Date: 2021-09-10

Abstract

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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References(15)

References

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