Feature selection for lung cancer detection using SVM based recursive feature elimination method

  • Authors:

  • Kesav Kancherla;Srinivas Mukkamala

  • Affiliations:

  • 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;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

  • Venue:
  • EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
  • Year:
  • 2012

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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.