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Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on the Improved Backpropagation Neural Network with Momentum

Authors :
Yi-jun Liu
Bei-hong Wang
Jiali Tang
Ming-fang Zhu
Dan Chen
Hong-fen Jiang
Xiang-jun Chen
Source :
International Journal Bioautomation, Vol 19, Iss 4, Pp 543-554 (2015)
Publication Year :
2015
Publisher :
Bulgarian Academy of Sciences, 2015.

Abstract

Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

Details

Language :
English
ISSN :
13141902 and 13142321
Volume :
19
Issue :
4
Database :
Directory of Open Access Journals
Journal :
International Journal Bioautomation
Publication Type :
Academic Journal
Accession number :
edsdoj.5f6718ed74bb4ed38f53351777f89deb
Document Type :
article