A New Expert System for Diagnosis of Lung Cancer: GDA—LS_SVM
- 425 Downloads
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.
KeywordsLung cancer General discriminant analysis Least squares support vector machine classifier Expert diagnosis system
- 2.http://www.cdc.gov/lungcancer/basic_info/index.htm (last accessed: 07 April 2006).
- 3.ftp://ftp.ics.uci.edu/pub/machine-learning-databases (last accessed: 05 March 2006).
- 8.Osareh,A., Mirmehdi, M., Thomas, B., Markham, R., Comparative exudate classification using support vector machines and neural networks. In: Dohi, T., and Kikinis, R. (Eds.), Fifth ınternational conference on medical ımage computing and computer-assisted ıntervention, lecture notes in computer science, vol. 2489. Berlin: Springer, 413–420, 2002.Google Scholar
- 9.Yao, Y., Frasconi, P., Pontil, M., Fingerprint classification with combinations of support vector machines, AVBPA 2001, LNCS 2091, 253–258, 2001.Google Scholar
- 12.Gunn, S R., Support vector machines for classification and regression. technical report, image speech and intelligent systems research group, University of Southampton, UK, 1998. Available on http://www.ecs.soton.ac.uk/~srg/publications/pdf/SVM.pdf [Accessed on 30 March 2006].
- 13.Kindermann, J., Paass, G., Leopold, E., Error correcting codes with optimized Kullback-Leibler distances for text categorization, PKDD 2001. 266–276, 2001.Google Scholar
- 14.Burges, C. J. C., A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 2(2): 121–167,1998.Google Scholar
- 15.Belousov, A. I., Verzakov, S. A., von Frese, J., Support vector machines: A versatile and powerful approach to data analysis, Poster at the Gordon Conf. on Statistics and Chemical Engineering, Williamstown, MA, 2001. Available on http://www.amstat.org./sections/spes/GRC2001.htm [Accessed on 28 March 2006].
- 16.Mustafa, H., Doroslovaki, M., Digital modulation recognition using support vector machine classifier, signals, systems and computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference. 2:2238–2242, 2004.Google Scholar
- 17.Avci, E., Avci, D., A novel approach for digital radio signal classification: Wavelet packet energy–multiclass support vector machine (WPE–MSVM). Expert Syst. Appl., 34(3): 2140–2147, (2008).Google Scholar
- 18.Avci, E., Turkoglu I., Poyraz, M., Intelligent target recognition based on wavelet adaptive network based fuzzy inference system, lecture notes in computer science, Springer-Verlag. 3522: 594–601, 2005.Google Scholar
- 20.Watkins, A., AIRS: A resource limited artificial immune classifier, Master thesis, Mississippi State University. 2001.Google Scholar