Study on the relationship between meteorological factors and the number of hypertension outpatients based on CNN-LSTM
10.16462/j.cnki.zhjbkz.2019.09.021
- Author:
Zhong-lin ZHANG
1
;
Shan-shan FEI
;
Xiao-lan REN
;
Jing ZHANG
Author Information
1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070， China
- Publication Type:Research Article
- Keywords:
Hypertension;
Meteorological elements;
Air pollutants;
Time series analysis;
CNN-LSTM
- From:
Chinese Journal of Disease Control & Prevention
2019;23(9):1126-1131
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the effect of meteorological factors on the number of hypertension outpatients in four areas of Gansu Province, then predict and analyze the trend of the number of hypertension outpatients, so as to provide reference for the prevention and control of hypertension diseases. Methods On the basis of controlling the confounding factors such as long-term trends, date effects, meteorological information and contaminant influence, a mixed model of convolutional neural network (CNN) and long-short term memory (LSTM) was constructed for the number of hypertension outpatients in the four regions of Baiyin, Chengxian, Qingcheng and Liangzhou by Python programming language. Results The root mean square errors of the CNN-LSTM model for the number of hypertensive outpatients in the four regions was 6.330 9, 6.814 2, 6.393 6 and 6.867 6. The mean absolute percentage error was 74.082 2, 78.508 2, 56.618 3 and 50.235 4. And the average absolute errors was 4.875 7, 5.431 1, 4.542 0 and 6.460 8. All the results was superior to those of support vector machine (SVM), autoregressive integrated moving average model (ARIMA), random forest (RF), CNN and LSTM. Conclusion The CNN-LSTM model can accurately predict the number of hypertension outpatients in Gansu. The hospital can rationally allocate medical resources according to the needs of hypertension for medical treatment at different times.
