[A preliminary study on the effects of meteorological factors on intracerebral hemorrhage death using the BP neural network model].

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 an 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 intracerebral hemorrhage mortality to predict the effect of BP neural network model, and to compare with the traditional multivariate linear regression model. RESULTS: Datas from the multivariate linear regression indicated that the cerebral hemorrhage death mortality had a negative correlation with maximum temperature and minimum humidity while having a positive correlation with the average relative humidity and the hours of sunshine. The linear correlation coefficient of intracerebral hemorrhage mortality was 0.7854, with mean absolute percentage (MAPE) as 0.21, mean square error (MSE) as 0.22, mean absolute error (MAE) as 0.19. The accuracy of forecasting was 81.31% with an average error rate as 0.19. The Fitting 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 as 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 multiple linear regression model on intracerebral hemorrhage mortality, using the data of 2010's.

Duke Scholars

Author Li Lan Molecular Genetics and Microbiology