中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (33): 6243-6246.doi: 10.3969/j.issn.1673-8225.2010.33.040

• 组织构建临床实践 clinical practice in tissue construction • 上一篇    下一篇

应用ARIMA模型预测青海海西州地区肺心病的发病率

田富鹏，马亮亮   

  1. 西北民族大学计算机科学与信息工程学院，甘肃省兰州市  730030
  • 出版日期:2010-08-13 发布日期:2010-08-13
  • 通讯作者: 马亮亮，硕士研究生，西北民族大学计算机科学与信息工程学院，甘肃省兰州市 730030 mll198684@126.com
  • 作者简介:田富鹏★，男，1968年生，青海省大同市人，藏族，1998年重庆大学毕业，硕士，硕士生导师，教授，主要从事数学模型方面的研究。 tfp@xbmu.edu.cn
  • 基金资助:

    国家自然科学基金资助项目（60673192），课题名称：基于数据挖掘的区域急诊疾病时谱空预测模型研究。

Application of ARIMA model in estimation of pulmonary heart disease in Qinghai Haixizhou region 

Tian Fu-peng, Ma Liang-liang   

  1. School of Computer and Information, Northwest University for Nationalities, Lanzhou  730030, Gansu Province, China
  • Online:2010-08-13 Published:2010-08-13
  • Contact: Ma Liang-liang, Studying for master’s degree, School of Computer and Information, Northwest University for Nationalities, Lanzhou 730030, Gansu Province, China mll198684@126.com
  • About author:Tian Fu-peng★, Master, Master’s supervisor, Professor, School of Computer and Information, Northwest University for Nationalities, Lanzhou 730030, Gansu Province, China tfp@xbmu.edu.cn
  • Supported by:

    the National Natural Science Foundation of China, No. 60673192*

PDF

682

被引次数

7

摘要：

背景：由于肺心病发病率受多种因素的影响，且各种因素之间又保持着错综复杂的联系。因此，用确定性数学模型来分析和预测比较困难。
目的：探讨ARIMA模型在时间序列资料中的应用，建立海西州地区肺心病发病率的预测模型。
方法：利用时间序列分析方法对海西州地区2003-01/2008-12肺心病发病率数据进行了分析。经过数据平稳化、模型识别确立了3种ARIMA模型，并借助于AIC和SC准则，选出了最佳模型ARIMA(2，1，1)。最后，对该模型进行了模型诊断检验和模型预测，确保了所建模型的合理性。
结果与结论：ARIMA(2，1，1)模型预测值的动态趋势和实际情况基本一致，整体效果不错，结果比较理想。提示：ARIMA(2,1,1)模型可作为海西州地区肺心病发病率的预测模型，且通过此模型可帮助人们了解肺心病发病率的发展趋势，有重点地对肺心病进行健康防治工作，有效地降低肺心病对人们的危害，保障人们的生活质量。

关键词: ARIMA模型, 模型识别, 残差独立性检验, 时间序列分析, 肺心病

Abstract:

BACKGROUND: The incidence rate of pulmonary heart disease is affected by many factors, and each kind of factor is maintaining the intriguing relation with others. Therefore, it is difficult to analyze and predict using deterministic mathematical model.
OBJECTIVE: To investigate the application of ARMA forecasting model on time series data, and to establish forecasting model on pulmonary heart disease incidence rate in Haixizhou region.
METHODS: The data of pulmonary heart disease incidence rate in Haixizhou from January 2003 to December 2008 were analyzed by time sequence. Three kinds of ARIMA models had been established through data stabilizing and mode identifying, and ARIMA (2, 1, 1) model has been selected out by virtue of the criterion of AIC and SC. At last, the rationality of the model was ensured by model test and model predication.
RESULTS AND CONCLUSION: The dynamic trend of prediction value forecasted by ARIMA (2, 1, 1) model basically accorded with the actual condition, with quite ideal results. ARIMA (2, 1, 1) model can be used as the forecasting of pulmonary heart disease incidence rate, which can help people comprehend the variation trend and regularity for seasonal change of pulmonary heart disease incidence rate, focused on the work of pulmonary heart disease healthy protection, effectively reduce the hazards of pulmonary heart disease to human, protection of human life quality.

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