Modeling analysis on the prediction of the cost of diseases
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    Abstract:

    On the basis of the per capita treatment cost per month of five common diseases, i.e. diabetes, intestinal polyp, hyperthyroidism, eutocia and cerebral infarction, in a 3-A-grade hospital in Chongqing from January, 2012 to December, 2014, we used BP neural network, generalized regression neural network (GRNN), grey system GM (1, 1) and non-linear regression analysis to predict the change of per capita treatment costs per month of these five diseases from January 2015 to August 2015. And the accuracy of these four models was judged by comparing the prediction results with real data. The results show that the minimum coefficients of determination (R2) of the four models are 0.278, 0.565, 0.048 and 0.097, respectively, while their maximum coefficients of determination(R2) are 0.826, 0.901, 0.600 and 0.747, respectively. The minimum prediction errors of the four models are 9.845%, 3.507%, 5.897% and 3.642%, respectively, while their maximum prediction errors are 15.450%, 13.940%, 30.518% and 17.204%, respectively. Compared with the other three models, the GRNN model can predict the cost of diseases more accurately.

    Reference
    [1] Tacettin Ornek. Clinical factors affecting the direct cost of patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease[J]. International Journal of Medical Sciences,2012,9(4):285-290.
    [2] Joel Segal. Cost of illness studies[M]. RTI International Center of Excellence in Health Promotion Economics,2006:2.
    [3] Kappelman M D, Rifas-Shiman S L, Porter C Q, et al. Direct Health Care Costs of Crohn's Disease and Ulcerative Colitis in US Children and Adults[J]. Gastroenterology,2008,135:1907-1913.
    [4] Usa C, Petcharat P, Nathorn C, et al. Factors affecting health-care costs and hospitalizations among diabetic patients in Thai public hospitals.[J]. Value in Health,2008,11(s1):69-74.,
    [5] Mehta S, Moore R D, Graham N M. Potential factors affecting adherence with HIV therapy[J]. Aids,1997,11(14):1665-1670.
    [6] 陈明.MATLAB神经网络原理与实例精解[M].北京:清华大学出版社,2013. CHEN Ming. MATLAB neural network principle and example[M]. Beijing:Tsinghua university press,2013.(in Chinese)
    [7] Asoodeh M, Shadizadeh S R, Zargar G. The estimation of stoneley wave velocity from conventional well log data:using an integration of artificial neural networks[J]. Energy Sources Part A:Recovery Utilization and Environmental Effects,2015,37(3):309-317.
    [8] Zhang J, Tan Z, Li C. A novel hybrid forecasting method using GRNN combined with wavelet transform and a GARCH model[J]. Energy Sources Part B:Economics Planning and Policy,2015,10(4):418-426.
    [9] Liu X Y, Peng H Q, Bai Y, et al. Tourism flows prediction based on an improved grey GM(1,1) model[J]. Procedia-Social and Behavioral Sciences,2014,138:767-775.
    [10] Khataee A, Vahid B, Behjati B, et al. Kinetic modeling of a triarylmethane dye decolorizeation by photoelectron-Fenton processs in a recirculateing system; Nonlinear regression analysis[J]. Chemical Engineering Research and Design,2013,92(2):362-367.
    [11] 张良均,曹晶,蒋世忠.神经网络实用教程[M].北京:机械工业出版社,2009:22-31. ZHANG Liangjun, CAO Jin, JIANG Shizhong. Neural network practical tutorial[M]. Beijing:China Machine Press,2009:22-31.(in Chinese)
    [12] 颜虹.医学统计学[M].2版.北京:人民卫生出版社,2010:215. YAN Hong. Medlical statistics[M]. 2th ed. Beijing:People's Medical Publishing House, 2010:215. (in Chinese)
    [13] 张德丰.MATLAB神经网络编程[M].北京:化学工业出版社,2011. ZHANG Defeng. MATLAB neural network programming[M]. Beijing:Chemical Industry Press,2011.(in Chinese)
    [14] 陈芳,楼文高.基于广义回归神经网络的蔬菜市场日价格预测[J].浙江农业学报,2015,27(7):1253-1258. CHEN Fang, LOU Wengao. Vegetable market day price forecasting based on generalized regression neural network[J]. Acta Agriculturae Zhejiangensis,2015,27(7):1253-1258.(in Chinese)
    [15] Liu B, Zhao L, Zhai Z J, et al. Optimum model of GM(1,1) and its suitable range[J]. Journal of Nanjing University of Aeronautics and Astronautics,2003,35(4):451-454.
    [16] 陈永胜.基于MATLAB和SPSS的非线性回归分析[J].牡丹江大学学报,2009,18(5):101-103. CHEN Yongsheng. Nonlinear regression analysis based on MATLAB and SPSS[J]. Journal of Mudanjiang University,2009,18(5):101-104.(in Chinese)
    [17] 杨华龙,刘金霞,郑斌.灰色预测GM(1,1)模型的改进及应用[J].数学的实践与认识,2011,41(23):39-46. YANG Hualong, LIU Jinxia, ZHENG Bin. Improvement and application of grey prediction GM (1,1) model[J]. Mathematics in Practice and Theory,2011,41(23):39-46.(in Chinese)
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张菁芳,李佳承,任家顺.疾病费用预测的建模分析[J].重庆大学学报,2016,39(2):99~106

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  • Received:November 23,2015
  • Online: May 16,2016
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