Mathematical model for predicting birth defect trend in Xi'an / 西安交通大学学报(医学版)

ABSTRACT

Objective: To understand the trend of birth defects in Xi'an by using gray model, ARIMA and NAR. Methods: The birth defects monitoring data of perinatal infants from 28-week pregnant women to 7 days after birth were collected from all the hospitals with obstetrical department in Xi'an during 2003 and 2015. The incidence of birth defects data from October 2003 to September 2015 in Xi'an City were used to construct the data model. We compared data with the actual birth defects rate from October 2003 to September 2015 to further optimize and make supplement for the model, and then predicted the incidence of birth defects in Xi'an from 2016 to 2017. Microsoft Excel 2003 was used for data input and SPSS version 16.0 was used for statistical analysis. Matlab was used for Gray Model and NAR prediction. ARIMA mathematical model was predicted by R software. Results: The grey prediction model suggested that the birth defects rate in the four quarters of 2016 and 2017 was 9.62‰, 9.67‰, 9.72‰, 9.77‰, 9.82‰, 9.87‰, 9.92‰ and 9.97‰, which was in slow increase. The ARIMA model predicted that the birth defects rate in Xi'an would still fluctuate at a high level in 2016 and 2017, and the rate in the four quarters was 11.98‰, 12.83‰, 11.28‰, 11.78‰, 12.23‰, 11.73‰, 11.80‰ and 12.00‰. The NAR model predicted that the birth defects rate in Xi'an was 13.24‰, 17.91‰, 10.55‰, 16.08‰, 16.47‰, 9.42‰, 11.99‰ and 11.68‰. The birth defects rate would reach the peak in 2016 and decrease in 2017. Comparison of the above three models showed that the root mean square error of grey prediction model, ARIMA model and NAR model was 1.353 009, 1.181 373 and 0.555 347, respectively. Conclusion: Based on the prediction by the above three mathematical models, it shows that NAR model is more accurate and reliable in predicting the data of this study, followed by ARIMA and grey model. Effective intervention measures for birth defects are still the key task of public health for a long time.