Artificial neural networks prediction for blood concentration and dosage of cyclosporine A

Author

Shan, Li ; Jie, Xia

Author_Institution

Coll. of Econ. & Manage., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

1875

Lastpage

1880

Abstract

This paper used methods of multiple linear regression (MLR), back propagation artificial neural network (BPANN) and genetic algorithm optimized back propagation artificial neural network (GA-BPANN) to predict blood concentration and dosage of cyclosporine A.It is proved that GABPNN model predict CsA blood concentration or dosage more accurate than MLR model or BPANN model by using 10-fold cross-validation. Besides, GABPANN model is more stable and reasonable. The scheme of two chained GABPANN models can be efficiently applied to prediction of CsA blood concentration and dosage.

Keywords

backpropagation; drugs; genetic algorithms; medical computing; neural nets; regression analysis; GA-BPANN; MLR; artificial neural network prediction; backpropagation; blood concentration; cyclosporine A; genetic algorithm; multiple linear regression; Accuracy; Blood; Data models; Genetic algorithms; Mathematical model; Predictive models; Training; bp artificial neural network; cyclosporine A; genetic algorithm; multiple linear regression;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6083945

Filename

6083945