J4 ›› 2012, Vol. 38 ›› Issue (2): 364-367.

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Artificial neural networks between actual bicarbonate reference value of Chinese women and geographical environment

CAO Xiao-yi1|GE Miao1|LIN Tian-ying1|FU Cheng-cheng2   

  1. 1.  |Department of Geography,School of Tourism and Environment,Shaanxi Normal University,Xi’an 710062,China;2. Department of Food Science,School of Food Engineering and Nutritional Science,Shaanxi Normal University,Xi’an 710062,China
  • Received:2011-09-14 Online:2012-03-28 Published:2012-03-28

Abstract:

Abstract:Objective To explore the relationship between the actual bicarbonate reference value of Chinese women and geographical environment,and provide scientific basis for  the distribution law of  actual bicarbonate of women reference value   and clinical  diagnosis.Methods 8 138 cases of actual bicarbonate reference values were collected.The relationship between reference values of actual bicarbonate of Chinese women and altitude,annual sunshine hours,annual mean air temperature,annual mean relative humidity,annual precipitation,annual range of air temperature were analyzed by correlation analysis.And the geographical factors were as nerve cell input back propagation(BP) neural networks to build nonlinear model.Results The correlation between the reference values of actual bicarbonate of Chinese women and 6 geographical factors was found,and there  were significant differences(P<0.05).The reference value of actual bicarbonate of women in 4 383 points all over China were calculated by a model which was established by 5-layer neural network and 3000 time’s training.The distribution map of the reference values of actual bicarbonate was drawn by spatial interpolation.Conclusion  The reference value of actual bicarbonate of women can be got by artificial neural networks (ANNs) model.The distribution map reflects that the spatial distribution law of   actual bicarbonate reference value in  western is lower than that in eastern of  China.

Key words: actual bicarbonate;reference value;geographical environment;artificial neural networks

CLC Number: 

  • R188