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Feature Selection for Lung Cancer Detection Using SVM Based Recursive Feature Elimination Method

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7246))

Abstract

Cancer is the uncontrolled growth of abnormal cells, which do not carry out the functions of normal cells. Lung cancer is the leading cause of death due to cancer in the world. The survival rate of cancer is about 15%. In order to improve the survival rate, we need an early detection method. In this study, we propose a new method for early detection of lung cancer using Tetrakis Carboxy Phenyl Porphine (TCPP) and well-known machine learning techniques. Tetrakis Carboxy Phenyl Porphine (TCPP) is a porphyrin that is able to label cancer cells due to the increased numbers of low density lipoproteins coating the surface of cancer cells and the porous nature of the cancer cell membrane.

In our previous work we studied the performance of well know machine learning techniques in the context of classification accuracy on Biomoda internal study. We used 79 features related to shape, intensity, and texture. We obtained an accuracy of 80% using the current feature set. In order to improve the accuracy of our method, we performed feature selection on these 79 features. We used Support Vector Machine (SVM) based Recursive feature Elimination (RFE) method in our experiments. We obtained an accuracy of 87.5% using reduced 19 feature set.

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Authors and Affiliations

  1. Institute for Complex Additive Systems and Analysis (ICASA), Computational Analysis and Network Enterprise Solutuons (CAaNES), New Mexico Institute of Mining and Technology, Socorro, New Mexico, 87801, U.S.A.

    Kesav Kancherla & Srinivas Mukkamala

Authors
  1. Kesav Kancherla
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  2. Srinivas Mukkamala
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Editor information

Editors and Affiliations

  1. Department of Animal Production Epidemiology and Ecology, University of Torino, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

  2. Universidade Nove de Lisboa, ISEGI, 1070-312 Lisboa, Portugal and University of Milano-Bicocca, D.I.S.Co., Viale Sarca 336, 20126 Milan, Italy

    Leonardo Vanneschi

  3. Center for Human Genetics Research, Department of Biomedical Informatics, Vanderbilt University, 519 Light Hall, 37232, Nashville, TN, USA

    William S. Bush

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© 2012 Springer-Verlag Berlin Heidelberg

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Kancherla, K., Mukkamala, S. (2012). Feature Selection for Lung Cancer Detection Using SVM Based Recursive Feature Elimination Method. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_15

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  • DOI: https://doi.org/10.1007/978-3-642-29066-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29065-7

  • Online ISBN: 978-3-642-29066-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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