期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
页码：435-442
出版社：Science and Information Society (SAI)
摘要：In the medical field to solve the new issues, the
novel approaches for managing relevant features by using
genomes are considered; using the sub-sequence of genes the
outcome of interest is analyzed. In this implementation part of
the model, we have given the MEDLINE and PubMed archives
as inputs to the proposed model. A large number of MESH terms
with gene and protein are utilized to characterize the patterns of
a large number of medical documents from a large set of records.
Standard datasets with different characteristics are used for
examination study. The characteristics and inadequacies of
different techniques are noted. Feature selection techniques are
given in perspective of data composes and region traits by
applying proper rules. Feature context extraction through name
element distinguishing proof is an essential errand of online
therapeutic report grouping for learning disclosure databases.
The parameters are identified to compare with other models
implemented on these datasets and the results prove that the
proposed method is very effective than existing models. The
primary point of the proposed ensemble learning models is to
characterize the high dimensional information for gene/proteinbased
disease expectation in light of substantial biomedical
databases. The proposed model uses an efficient ranking
algorithm to select the relevant attributes from a set of all
attributes; the attributes are given to the classifier to improve the
accuracy based on the users’ interest.
