关键词: 支持向量机, 肾综合征出血热, 预测

Abstract: Objective To study the superiority and application prospect of support vector machine(SVM) on the forecast of the incidence of hemorrhagic fever with renal syndrome(HFRS).Methods Firstly,the routine meteorological data of Huludao city including average air pressure,average temperature,relative humidity,precipitation and sunshine time and the epidemiologic information of animal disease including rodent density and rodents borne virus from 1984 to 2006 were used as predictable variables.All the variables were limited to the range from 0 to 1.The whole data atlas were separated into training atlas and test atlas.The test atlas were made up of 1/3 individuals(trunc) randomly sampled from data atlas,and other samples were composed of training atlas.Secondly,SVM was applied to the HFRS incidence prediction and the SVM model was constructed by software R2.60.Finally,the performance of SVM,back-propagation(BP) and radial basis function(RBF) Neural Networks were compared by computing the sum square error(SSE).The above procedures were repeated for 10 replications.Results The mean and standard diviation of SSE of SVM for training atlas was(0.031±0.009),while those of BP and RBF neural network were(0.074±0.030) and(0.082±0.018),respectively.For the test atlas,the mean and standard diviation of SSE of SVM was(0.067±0.021),while those of BP and RBF neural network were(0.073±0.022) and(0.089±0.036),respectively.Conclusion As a new pattern recognition method developed on the basis of statistics theory in recent years,SVM had higher forecast precision and stronger generalization ability to solve the small sample size and the indentification of nonlinear and high-dimension model,SVM was reliable for the prediction of HFRS incidence,which could serve as a reference method for the HFRS prediction.