Predictive effect of back propagation neural network model on hematoma enlargement in patients with cerebral hemorrhage
Wu Gang
Xu Guoyu
Bai Ying
Zhou Qing
Liu Ce
Chang Pengfei
Abstract：Objective To study predicting results of the back propagation (BP)neural network model for hematoma enlargement (HE)in patients with intracerebral hemorrhage. Methods The clinical data of 128 patients with cerebral hemorrhage admitted to the 309th hospital of People′s Liberation Army from January 2011 to December 2014 were analyzed retrospectively. The Matlab 7. 14 software was used to achieve BP neural network model for predicting hematoma enlargement within 24 hours in patients with intracerebral hemorrhage (HE ≥6. 0 ml and HE ≥12. 5 ml). The mean square error (MSE)of the model and the accuracy of the overall prediction were calculated. The receiver operation characteristic (ROC) curve was drawn for predicting HE. Results When the BP neural network predicted HE ≥6. 0 ml and HE ≥12. 5 ml,the mean square deviations of the training set,validation set,and test set were 0. 061, 0. 143,0. 052 and 0. 023,0. 057,and 0. 065,respectively. The best fitting performance verification of hematoma enlargement was as follows:≥ 6. 0 ml for network training 11 times and the error value 0. 224;≥12. 5 ml for network training 20 times,and the error value 0. 057. The overall accuracies of predicting HE ≥6. 0 ml and HE ≥12. 5 ml were 92. 2% (118/ 128)and 96. 9% (124/ 128)respectively. Conclusion The BP neural network model have no special limitation for data. It can accurately fit the hematoma expansion model of cerebral hemorrhage.
Keywords：Cerebral hemorrhageBack propagation neural networkHematoma enlargementPrediction
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 505-510 )