A comparative study of the RNN, the JPR, and ARIMA for predicting maternal mortality ratio in rural areas in China

YIN Xiaolan; HE Xinxin; DU Lin; LI Yuansheng; ZHANG Junhui

doi:10.16462/j.cnki.zhjbkz.2023.11.011

Volume 27 Issue 11

Nov. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(11): 1308-1313

YIN Xiaolan, HE Xinxin, DU Lin, LI Yuansheng, ZHANG Junhui. A comparative study of the RNN, the JPR, and ARIMA for predicting maternal mortality ratio in rural areas in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1308-1313. doi: 10.16462/j.cnki.zhjbkz.2023.11.011

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YIN Xiaolan, HE Xinxin, DU Lin, LI Yuansheng, ZHANG Junhui. A comparative study of the RNN, the JPR, and ARIMA for predicting maternal mortality ratio in rural areas in China[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1308-1313. doi: 10.16462/j.cnki.zhjbkz.2023.11.011

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A comparative study of the RNN, the JPR, and ARIMA for predicting maternal mortality ratio in rural areas in China

doi: 10.16462/j.cnki.zhjbkz.2023.11.011

Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, Luzhou 646000, China

Funds:

National Statistical Science Research Project of China 2021LZ31

Scientific Research Project of Southwest Medical University 2021ZKMS002

More Information

Corresponding author: ZHANG Junhui, E-mail: vzjh960500@163.com
Received Date: 2022-07-13
Rev Recd Date: 2022-10-02

Available Online: 2023-11-20

Publish Date: 2023-11-10

Abstract

Abstract

Objective To explore the application value of the recurrent neural network (RNN) model, the joinpoint regression (JPR) model, and the autoregressive integrated moving average (ARIMA) model on predicting maternal mortality ratio (MMR) in rural areas in China, and to make statistical predictions on whether the MMR decrease targets of "Healthy China 2030" and other documents will be achieved or not. Methods The RNN, JPR, and ARIMA models were constructed based on the data of MMR in rural areas in China from 2000 to 2019, and the mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) were used to compare the back generation fitting effects of the three models. The residuals and relative errors were used to compare the point prediction effects of the three models for MMR data in rural areas in 2020. Finally, the optimal model was selected to forecast the MMRs in rural areas from 2021 to 2030. Results From 2000 to 2020, the MMRs in rural areas in China showed an overall continuous downward trend. The back generation fitting effects of the three models were ranked in descending order as follows: RNN > JPR > ARIMA, and the MAE, MSE, and RMSE of the RNN models were all less than 0.02. The accuracies of the three models for the point prediction of rural MMR in 2020 were ranked in descending order as follows: RNN > JPR > ARIMA. The prediction results of the optimal RNN model showed that the rural MMR in 2022 would be 18.02/100 000, indicating the decreased target of the relevant documents would be achieved in 2022. The MMR in 2025 and 2030 would be 17.58/100 000 and 17.27/100 000, respectively, indicating the decrease targets of the "Health China 2030" and other documents would not be achieved in 2025 and 2030. Conclusions The predictive performance of the RNN model is much better than those of the JPR model and the ARIMA model. The JPR model is an acceptable predictive model, while the ARIMA model is less suitable for the prediction of this data.
- Maternal mortality ratio,
- Recurrent neural network model,
- Joinpoint regression model,
- Autoregressive integrated moving average,
- Prediction

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References

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