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Reseach Article

Knowledge Discovery by Decision Tree to Model the Rate of Drug Release from Matrix Substrates

by Parastoo Namdarian, Azadeh Asefnejad, Fardad Farokhi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 7
Year of Publication: 2016
Authors: Parastoo Namdarian, Azadeh Asefnejad, Fardad Farokhi

Parastoo Namdarian, Azadeh Asefnejad, Fardad Farokhi . Knowledge Discovery by Decision Tree to Model the Rate of Drug Release from Matrix Substrates. International Journal of Computer Applications. 138, 7 ( March 2016), 54-58. DOI=10.5120/ijca2016908992

@article{ 10.5120/ijca2016908992,
author = { Parastoo Namdarian, Azadeh Asefnejad, Fardad Farokhi },
title = { Knowledge Discovery by Decision Tree to Model the Rate of Drug Release from Matrix Substrates },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 7 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 54-58 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908992 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:39:06.165211+05:30
%A Parastoo Namdarian
%A Azadeh Asefnejad
%A Fardad Farokhi
%T Knowledge Discovery by Decision Tree to Model the Rate of Drug Release from Matrix Substrates
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 7
%P 54-58
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

To study the effect of a drug on a target tissue, mainly all process of drug delivery from drug delivery systems to the transferred drug in whole body must be considered. These studies are very attractive for different fields of drug delivery systems and pharmaceutical industry. Modeling can help us for better understanding of this process and reaching to better design of system. In this paper, we extract the rules of drug delivery by decision tree for a specific systems such as Matrix substrate. The results show that we can achieve to %98.04 and %74.51 accuracy, if we use decision tree as a classifier and a predictor respectively.

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Index Terms

Computer Science
Information Sciences


Drug Release Modeling Decision Tree Matrix Substrates