Comparing performance of C5.0 decision tree and radial basis function neural network for predicting hemorrhagic transformation in patients with acute ischemic stroke
10.16462/j.cnki.zhjbkz.2019.02.021
- Author:
Hai-dong WANG
1
;
Lu ZHANG
;
Jie WANG
;
Jing LI
;
Ying ZHOU
;
Guo-li WANG
;
Ke-ke WANG
;
Yan-bo PENG
;
Jian-hui WU
Author Information
1. Department of Epidemiology and Health Statistics,School of Public Health, North China University of Science and Technology, Hebei Key Laboratory of Coal Mine Health and Safety, Tangshan 063210,China
- Publication Type:Journal Article
- Keywords:
C5.0 decision tree;
RBF neural network;
Acute ischemic stroke;
Hemorrhagic transformation;
Performance
- From:
Chinese Journal of Disease Control & Prevention
2019;23(2):227-232
- CountryChina
- Language:Chinese
-
Abstract:
Objective To compare performance of C5.0 decision tree models and radial basis function(RBF) neural network in predicting the risk of hemorrhagic transformation in acute ischemic stroke. Methods Patients with acute ischemic stroke admitted to hospital were enrolled. Hemorrhagic transformation group and non-hemorrhagic transformation group were divided according to whether hemorrhagic transformation occurred within 2 weeks after admission. Retrospectively collected patients’ case information. C5.0 decision tree models and RBF neural network model were established with the ratio of 7 :3 for training set and test set, and the prediction performance of the model was compared. Results A total of 460 patients’ case information were collected and divided in 314 training set samples and 146 test set samples. Accuracy rates of the C5.0 decision tree model were 96.5% and 80.1%, sensitivities were 98.1% and 82.6%, specificities were 94.8% and 77.9%, Kappa index were 0.93 and 0.60, and AUC were 0.97 and 0.80. Accuracy rates of the neural network model were 72.6% and 74.7%, sensitivities were 87.6% and 88.4%, specificities were 56.9% and 62.3%, Kappa index were 0.45 and 0.50, and AUCs were 0.72 and 0.75. In the training set, the prediction performance of the C5.0 decision tree model was superior to the RBF neural network model. However, there was no statistical difference in the test set.Conclusion C5.0 decision tree model is better than RBF neural network model in risk prediction.
