| 高菡璐,兰莉,乔冬菊,赵娜,杨佳琦,邵冰,焦喆,李航,王滨有.BP神经网络模型用于气象因素对脑出血死亡影响的初步研究[J].中华流行病学杂志,2012,33(9):937-940 |
| BP神经网络模型用于气象因素对脑出血死亡影响的初步研究 |
| A preliminary study on the effects of meteorological factors oil intracerebral hemorrhage death using the BP neural network model |
| 收稿日期:2012-03-19 出版日期:2014-09-17 |
| DOI：10.3760/cma.j.issn.0254-6450.2012.09.014 |
| 中文关键词: BP神经网络 脑出血 气象 预测 |
| 英文关键词: BP neural network Intracerebral hemorrhage Meteorology Forecas |
| 基金项目:黑龙江省卫生厅科研项目(2009—544) |
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| 中文摘要: |
| 目的 探讨BP神经网络预测模型存分析气象因素与脑出血死亡率关系中的应用。方法 根据BP神经网络的特性，利用MATLAB 7.0软件的神经网络工具箱对2007—2009年哈尔滨市气象数据建立脑出血死广率的BP神经网络预报模型，并与传统的多元线性回归模型进行比较。结果 利用多元线性回归结果显示脑出血死亡率与最高气温、最小相对湿度呈负相关，与平均相对湿度、日照时数呈正相关。脑出血死亡率的非线性相关系数(RNL)为0.7854，平均绝对误差百分比(MAPE)为0.2l，均方误差(MSE)为0.22，平均绝对识差(MAE)为0.19，预测准确度(P)为81.31%．平均误差率为0.19。BP神经网络模型的拟合结果冠示，脑出血死亡率的RNL为0.7967，MAPE为0.19，MSE为0.21，MAE为0.18，P为82.53%，平均误差率为0.17。结论 应用BP神经网络预测模型对2010年哈尔滨市脑出血死亡率进行预报，通过与多元线性回归模型预报结果进行比较，表明该模型具有更高的预报准确度。 |
| 英文摘要: |
| Objective Using the Back Propagation (BP) Neural Network Model to discover the relationship between meteorological factors and mortality of intracerebral hemorrhage, to provide evidence for developing all intracerebral hemorrhage prevention and control program, in Harbin. Methods Based on the characteristics of BP neural network, a neural network Toolbox of MATLAB 7.0 software was used to build Meteorological data of 2007-2009 with intracarebral hemorrhage mortality to predict the effect of BP neural network model, and to compare with the traditiona multivariate linear regression model. Results Datas from the multivariate linea regression indicated that the cerebral hemorrhage death mortality had a negative correlation with maximum temperatureand minimum humidity while having a positive correlation with the average relative humidity and the hours of sunshine. The linear correlation coefficient of intracarebral hemorrhage mortality was 0.7854, with mean absolute percentage (MAPE) as 0.2l, mean square error (MSE)as 0.22, mean absolute error (MAE) as 0.19. The accuracy offorecasting was 81.31% with an average error rate as 0.19. The Fittihg results of BP neural network model showed that non-linear correlation coefficient of intracerebral hemorrhage mortality was 0.7967, with MAPE as 0.19, MSE 0.21, MAE as 0.18. The forecasting accuracy was 82.53%, with the average error rate as 0.17. Conclusion The BP neural network model showed a higher forecasting accuracy when compared to the muhiplc lincar regression model on intracerebral hemorrhage mortality, using the data of 2010's. |
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