A New Expert System for Diagnosis of Lung Cancer: GDA—LS_SVM

In nowadays, there are many various diseases, whose diagnosis is very hardly. Lung cancer is one of this type diseases. It begins in the lungs and spreads to other organs of human body. In this paper, an expert diagnostic system based on General Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) Classifier for diagnosis of lung cancer. This expert diagnosis system is called as GDA-LS-SVM in rest of this paper. The GDA-LS-SVM expert diagnosis system has two stages. These are 1. Feature extraction and feature reduction stage and 2. Classification stage. In feature extraction and feature reduction stage, lung cancer dataset is obtained and dimension of this lung cancer dataset, which has 57 features, is reduced to eight features using Generalized Discriminant Analysis (GDA) method. Then, in classification stage, these reduced features are given to Least Squares Support Vector Machine (LS-SVM) classifier. The lung cancer dataset used in this study was taken from the UCI machine learning database. The classification accuracy of this GDA-LS-SVM expert system was obtained about 96.875% from results of these experimental studies.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[2]  Enrique Frías-Martínez,et al.  Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition , 2006, Eng. Appl. Artif. Intell..

[3]  Kemal Polat,et al.  Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer , 2008, Expert Syst. Appl..

[4]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[5]  Engin Avci,et al.  Intelligent target recognition based on wavelet packet neural network , 2005, Expert Syst. Appl..

[6]  Engin Avci,et al.  Intelligent Target Recognition Based on Wavelet Adaptive Network Based Fuzzy Inference System , 2005, IbPRIA.

[7]  A. B. Watkins,et al.  A resource limited artificial immune classifier , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[8]  Vincent Baeten,et al.  Combination of support vector machines (SVM) and near‐infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds , 2004 .

[9]  Engin Avci,et al.  A novel approach for digital radio signal classification: Wavelet packet energy-multiclass support vector machine (WPE-MSVM) , 2008, Expert Syst. Appl..

[10]  Gerhard Paass,et al.  Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization , 2001, PKDD.

[11]  Yuan Yao,et al.  Fingerprint Classification with Combinations of Support Vector Machines , 2001, AVBPA.

[12]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Majid Mirmehdi,et al.  Comparative Exudate Classification Using Support Vector Machines and Neural Networks , 2002, MICCAI.

[15]  Emre Çomak,et al.  A decision support system based on support vector machines for diagnosis of the heart valve diseases , 2007, Comput. Biol. Medicine.

[16]  H. Mustafa,et al.  Digital modulation recognition using support vector machine classifier , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..