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Multiclass lung cancer diagnosis by gene expression programming and microarray datasets

conference contribution
posted on 2017-11-05, 00:00 authored by Hasseeb Dawood Injas Azzawi, Jingyu HouJingyu Hou, Russul Al-AnniRussul Al-Anni, Yong XiangYong Xiang, R Abdu-Aljabar, A Azzawi
There are various types of lung cancer and they can be differentiated
by the cell size as well as the growth pattern. They are all treated differently. Classification of the various types of lung cancer assists in determining the specified treatments to decrease the fatality rates. In this paper, we broaden the analysis of lung by using gene expression data, binary decomposition strategies and Gene Expression Programming (GEP) technique, aiming at achieving better classification performance. Classification performance was assessed and compared between our GEP models and three representative machine learning techniques, SVM, NNW and C4.5 on real microarray Lung tumor datasets. Dependability was evaluated by the cross-informational collection validation. The evaluation results demonstrate that our technique can achieve better classification performance in terms of Accuracy, standard deviation and range under the recipient working trademark bend. The proposed technique in this paper provides a helpful tool for Lung cancer classification.

History

Event

Advanced data mining and applications. International conference (13th : 2017 : Singapore)

Volume

10604

Series

Lecture notes in computer science

Pagination

541 - 553

Publisher

Springer

Location

Singapore

Place of publication

Cham, Switzerland

Start date

2017-11-05

End date

2017-11-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319691787

ISBN-10

3319691791

Language

English

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2017, Springer International Publishing AG

Editor/Contributor(s)

G Cong, W-C Peng, W Zhang, C Li, A Sun

Title of proceedings

ADMA 2017 : Proceedings of the Advanced Data Mining and Applications International Conference