灰色模型GM(1,1)在出生缺陷预测中的应用研究
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摘要
目的探讨灰色模型GM(1,1)在预测不同水平出生缺陷发生率中的应用问题及数据波动性对预测效果的影响。方法利用2009年10月至2016年9月西安市出生缺陷监测数据,构建总出生缺陷及前5种缺陷的月、季度、年共3个层次出生缺陷发生率的GM(1,1)灰色模型,比较不同层次预测模型的拟合精度。结果总出生缺陷年度预测时,平均相对误差为4.6%,均方差比为0.259,提示其预测效果较好;按季度预测时效果勉强合格,平均相对误差为10.2%;按月进行预测时,效果较差,平均相对误差为17.5%。西安市前5种出生缺陷(先天性心脏病、唇腭裂、神经管畸形、多指、先天性脑积水)灰色模型预测结果都随着预测时间单位的变大,拟合精度逐渐提高,年预测模型拟合精度最好。结论灰色模型的预测效果可能与数据波动性有关,对出生缺陷发生率进行预测时以年度为单位的结果可能较适宜。
Objective To investigate the application of gray model GM(1,1)in predicting the incidence of birth defects at different levels and the effect of data volatility on the prediction outcome.Methods Based on the monitoring data of birth defects in Xi'an from October 2009 to September 2016,the GM(1,1)was used to predict the overall incidence of birth defects and incidence of five main birth defects at three levels(month,quarter,and year).We compared the fitting accuracy of different level prediction models.Results The average relative error for yearly prediction of overall birth defect was 4.6%,and the mean square deviation was 0.259,which might suggest better prediction.Quarterly forecasting results were almost qualified and the average relative error was 10.2%.Monthly prediction was poor with an average relative error of 17.5%.With the extension of the forecast period,the grey model prediction results of the top five birth defects(congenital heart disease,cleft lip and palate,neural tube defects,multiple fingers,and congenital hydrocephalus)in Xi'an all increased,and the fitting accuracy gradually improved.The gray scale of the year was the best.Conclusion The prediction results of the gray model may be related to the volatility of the data.It may be suitable for predicting the incidence of birth defects by the year.
Objective To investigate the application of gray model GM(1,1)in predicting the incidence of birth defects at different levels and the effect of data volatility on the prediction outcome.Methods Based on the monitoring data of birth defects in Xi'an from October 2009 to September 2016,the GM(1,1)was used to predict the overall incidence of birth defects and incidence of five main birth defects at three levels(month,quarter,and year).We compared the fitting accuracy of different level prediction models.Results The average relative error for yearly prediction of overall birth defect was 4.6%,and the mean square deviation was 0.259,which might suggest better prediction.Quarterly forecasting results were almost qualified and the average relative error was 10.2%.Monthly prediction was poor with an average relative error of 17.5%.With the extension of the forecast period,the grey model prediction results of the top five birth defects(congenital heart disease,cleft lip and palate,neural tube defects,multiple fingers,and congenital hydrocephalus)in Xi'an all increased,and the fitting accuracy gradually improved.The gray scale of the year was the best.Conclusion The prediction results of the gray model may be related to the volatility of the data.It may be suitable for predicting the incidence of birth defects by the year.
引文
[1]党少农,颜虹,王红丽.出生缺陷的流行态势和影响因素及人群研究的若干问题[J].西安交通大学学报(医学版),2017,38(03):317-325.
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[5]樊慧,栗河舟,郝长付.某医院2010年至2014年超声诊断的六大类致死性畸形发病情况及ARIMA预测模型的建立[J].郑州大学学报(医学版),2015,50(3):351-354.
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[9]邵珠艳,王春梅,魏曼莎.灰色GM(1,1)预测模型在疾病预测中的应用[J].中国医院统计,2003,10(3):146-148.
[10]张刚伟,谢和宾.某医院出院人数的GM(1,1)灰色预测及SPSS程序实现[J].数理医药学杂志,2012,25(6):714-716.
[11]肖德才,符湘云,胡筱,等.基于SAS的住院量灰色预测及实现[J].中国卫生统计,2012,29(4):586-587.
[12]庞艳蕾,张惠兰,李向云,等.灰色模型GM(1,1)和ARIMA在拟合全国婴儿、5岁以下儿童死亡率中的应用[J].中国卫生统计,2015,32(3):461-436.
[13]梁玉红,李同博,周令,等.中国出生缺陷趋势预测分析[J].中国公共卫生,2014,30(6):779-780.
[14]李玲,王培承,马宝銮,等.应用灰色模型预测围产儿出生缺陷发病趋势[J].中国医院统计,2008,1(15):21-23.
[15]ZHU JL,MADSEN KM,VESTERGAARD M,et al.Paternal age and congenital malformations[J].Hum Reprod,2005,20(11):3173-3177.
[16]PADULA AM,TAGER LB,CARMICHAEL SL,et al.Ambient air pollution and traffic exposures and congenital heart defects in the San Joaquin Valley of California[J].Paediatr Perina Epidemiol,2013,27(4):329-339.
[17]BERRY RJ,LI Z,ERICKSON JD,et al.Prevention of neuraltube defects with folic acid in China[J].N Engl J Med,1999,341(20):1485-1490.
[2]章琦,相晓妹,宋辉,等.2010-2015年西安市围产儿出生缺陷的流行特征[J].西安交通大学学报(医学版),2017,38(3):317-325.
[3]李宏艳,汤旭钢,张泽勤,等.先天出生缺陷相关因素研究结果分析[J].宁夏医学杂志,2010,32(8):717-718.
[4]张丽,米白冰,相晓妹,等.自回归求和移动平均乘积季节模型在西安地区出生缺陷预测中的应用[J].西安交通大学学报(医学版),2017,38(3):317-325.
[5]樊慧,栗河舟,郝长付.某医院2010年至2014年超声诊断的六大类致死性畸形发病情况及ARIMA预测模型的建立[J].郑州大学学报(医学版),2015,50(3):351-354.
[6]杨丽,张毅.中国出生缺陷预测模型及比较[J].中国妇幼保健,2017,32(4):643-645.
[7]刘晓冬,孟祥臻,景睿,等.应用GM(1,1)灰色模型预测新生儿出生缺陷率[J].中国优生与遗传杂志,2008,16(9):98-99.
[8]孙金杰,马英豪,李沪建.围产儿出生缺陷发病率的灰色预测及SAS实现[J].中国卫生统计,2010,27(3):311-312.
[9]邵珠艳,王春梅,魏曼莎.灰色GM(1,1)预测模型在疾病预测中的应用[J].中国医院统计,2003,10(3):146-148.
[10]张刚伟,谢和宾.某医院出院人数的GM(1,1)灰色预测及SPSS程序实现[J].数理医药学杂志,2012,25(6):714-716.
[11]肖德才,符湘云,胡筱,等.基于SAS的住院量灰色预测及实现[J].中国卫生统计,2012,29(4):586-587.
[12]庞艳蕾,张惠兰,李向云,等.灰色模型GM(1,1)和ARIMA在拟合全国婴儿、5岁以下儿童死亡率中的应用[J].中国卫生统计,2015,32(3):461-436.
[13]梁玉红,李同博,周令,等.中国出生缺陷趋势预测分析[J].中国公共卫生,2014,30(6):779-780.
[14]李玲,王培承,马宝銮,等.应用灰色模型预测围产儿出生缺陷发病趋势[J].中国医院统计,2008,1(15):21-23.
[15]ZHU JL,MADSEN KM,VESTERGAARD M,et al.Paternal age and congenital malformations[J].Hum Reprod,2005,20(11):3173-3177.
[16]PADULA AM,TAGER LB,CARMICHAEL SL,et al.Ambient air pollution and traffic exposures and congenital heart defects in the San Joaquin Valley of California[J].Paediatr Perina Epidemiol,2013,27(4):329-339.
[17]BERRY RJ,LI Z,ERICKSON JD,et al.Prevention of neuraltube defects with folic acid in China[J].N Engl J Med,1999,341(20):1485-1490.