Research of cardiovascular diseases prediction model based on meteorological and environmental factors

Objective To compare the performance of stepwise regression and artificial neural model in establishing the cardiovascular disease prediction model base on meteorological and environmental factors.Methods A total of 23 input variables was made,including 12 input meteorological variables from daily routine observation of Beijing during 2008-2011 and five variables constructed from them,concentrations of three pollutants in the same period,as well as dummy variables for three years.Output variable of the model was the daily count of emergency admissions due to cardiovascular diseases,which extracted from the original daily records of a hospital in Beijing.All the data were averaged by week and formed 80% of the data as training set,while the remaining 15% was comprised of test set,while formed the other 5% as validation set.The stepwise regression models and the artificial neural network model were constructed respectively and the prediction performance of the models was compared by the independent samples(validation set).Results The stepwise regression process selected 11 variables as input,while the artificial neural network model structure was 23-22-1.The average absolute error,average error,minimum error and the Pearson Correlation of neural network prediction model was 0.914 9,-0.003 3,0.01,0.873,which were superior to stepwise regression model in forecasting performance metrics of the validation set.Conclusion Artificial neural network method has more advantages to traditional statistical method in establishing the prediction model of cardiovascular diseases based on meteorological and environmental factors.