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International Conference on Advanced Data Mining and Applications

ADMA 2017: Advanced Data Mining and Applications pp 541–553Cite as

Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

Abstract

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.

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Author information

Authors and Affiliations

  1. School of Information Technology, Deakin University, Burwood, VIC, Australia

    Hasseeb Azzawi, Jingyu Hou, Russul Alanni & Yong Xiang

  2. Information Engineering College, Al-Nahrain University, Baghdad, Iraq

    Rana Abdu-Aljabar

  3. Iraqi Ministry of Education, Baghdad, Iraq

    Ali Azzawi

Authors
  1. Hasseeb Azzawi
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  2. Jingyu Hou
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  3. Russul Alanni
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  4. Yong Xiang
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  5. Rana Abdu-Aljabar
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  6. Ali Azzawi
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Corresponding author

Correspondence to Hasseeb Azzawi .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Azzawi, H., Hou, J., Alanni, R., Xiang, Y., Abdu-Aljabar, R., Azzawi, A. (2017). Multiclass Lung Cancer Diagnosis by Gene Expression Programming and Microarray Datasets. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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