Design of Decision Support System in the Metastatic Colorectal Cancer Data Set and Its Application

Yıl 2016, Cilt: 4 Sayı: 1, 12 - 16, 30.03.2016

Tuba Pala Ali Yilmaz Camurcu

Öz

Developing the system which will help doctors with the result to be obtained from the medical data sets by realizing the design of the medical decision support system in which data mining methods are used is the primary objective of this study.

The survivability condition of metastatic colorectal cancer disease has been predicted using data mining methods in the developed system. In the process of data mining, after the phase of data preprocessing, Support Vector Machines, Naive Bayes, Decision Trees, Artificial Neural Networks, Multilayer Perceptron, Logistic Regression algorithms have been used.

In the study, two different medical decision support system models, classifying prediction and hybrid models, have been developed, and the results obtained from the two models have been compared after being examined. While the most successful algorithm is Support Vector Machines in Classifying Prediction Model in which only classification algorithms are applied, it is Decision Trees and Artificial Neural Networks which are the most successful algorithms with an accuracy rate of 100 per cent in Hybrid Prediction Model. In consequence of the classifying processes, when the accuracy rates of the models are examined, it is seen that while the accuracy rate of Classifying Prediction Model is 65-70%, this rate reaches 95-100% in Hybrid Prediction Model. It has been seen that accuracy rates have elicited very high and very close values for all of the algorithms with the realized hybrid structure, that is, with clustering, in the applications of the classifying algorithms combined.

Anahtar Kelimeler

Medical Decision Support System, Data Mining, Classification, K-Means Clustering, Metastatic Colorectal Cancer Disease Data Set

Kaynakça

• [1] http://kolonkanseri.blogspot.com/p/kolorektal-kanser-nedir.html: Kolorektal Kanser Nedir ? (Son erişim: 06.10.2012). [2] B. Moghimi-Dehkordi, A. Safaee, “An overview of colorectal cancer survival rates and prognosis in Asia”, World J Gastrointest Oncol Vol.4, No.4, pp.71-75, 2012. [3] S. Grumett, P. Snow, and D. Kerr”, Neural Networks in the Prediction of Survival in Patients with Colorectal Cancer”, Clinical Colorectal Cancer, Vol.2, No.4, 239-244, 2003. [4] A. Biglarian, E. Bakhshi, M.R. Gohari, R. Khodabakhshi, “Artificial Neural Network for Prediction of Distant Metastasis in Colorectal Cancer”, Asian Pacific Journal of Cancer Prevention, Vol.13, pp.927- 930, 2012. [5] S. Shenbaga Ezhil, and C. Vijayalakshmi, “Prediction Of Colon- Rectum Cancer Survivability Using Artificial Neural Network”, International Journal of Computer Engineering and Technology (IJCET), Vol.3, No.1, January- June 2012. [6] S.K. Fathy, “A Predication Survival Model for Colorectal Cancer”, proceeding AMERICAN-MATH’11/CEA’11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications, pp.36-42, 2011. [7] M. Dener, M. Dorterler, and A. Orman, "Açık Kaynak Kodlu Veri Madenciliği Programları: WEKA’da Örnek Uygulama", Akademik Bilişim’09 - XI. Akademik Bilişim Konferansı, Harran University, 11- 13 February 2009 Şanlıurfa. (in Turkish). [8] J.R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, California, 1993. [9] C. Beh Boon, M.Z.M. Jafri, S. Lim Hwee, "Mangrove Mapping in Penang Island by Using Artificial Neural Network Technique", Open Systems (ICOS), 2011 IEEE Conference on, pp.245-249, 2011. [10] D. Delen, G. Walker, A. Kadam, “Predicting breast cancer survivability: a comparison of three data mining methods”, Artificial Intelligence in Medicine, Vol 34, pp.113-127, June 2005. [11] R. Jiangtao, L. Sau Dan, C. Xianlu, K. Ben, R. Cheng, and D. Cheung, "Naive Bayes Classification of Uncertain Data", Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on, pp.944-949. [12] A. Menon Krishna, “Large-Scale Support Vector Machines: Algorithms and Theory”, Research Exam, University of California, San Diego, 2009. [13] H. Arslan, “Web Mining and Data Analysis of a Web Site, Sakarya University, Master Thesis, Sakarya, Turkey, 2008. (in Turkish).