Diagnosis of Mesothelioma Disease Using Different Classification Techniques
DOI:
https://doi.org/10.18201/ijisae.2017SpecialIssue31416Keywords:
Artificial Neural Network, Classification Algorithms, Classification Ratio, Data Mining, Mesothelioma DiseaseAbstract
Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio.Downloads
References
H.I. Witten, E. Frank, “Data Mining, Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, USA; 2005.
C. Coşkun, A. Baykal, “An Application for Comparison of Data Mining Classification Algorithms”, XIII. Akademik Bilişim Konferansı, February 2011
D. J. Hand, “Classifier Technology and the Illusion of Progress”, Statistical Science, Vol. 21; Institute of Mathematical Statistics, pp. 1-15, 2006
L Salzberg, “Methodological Note On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach”, Data Mining and Knowledge Discovery 1, Kluwer Academic Publishers, Boston, pp. 317–328, 1997
D. Michie, D. J. Spiegelhalter, “Machine Learning, Neural and Statistical Classification”, Taylor, C. C., Prentice Hall, 1994.
R. L. Wilson, R. Sharda, “Bankruptcy prediction using neural networks”, Decision Support Systems, Vol. 11, Issue 5, Elsevier Science Publishers B. V. Amsterdam, The Netherlands, pp. 545-557, 1994
M. Lin, S. Huang, Y. Chang, “Kernel-based discriminant technique for educational placement”, Journal of Educational and Behavioral Statistics, Vol 29, pp. 219-240, 2004
R. D. King, C. Feng, A. Sutherland, “StatLog: Comparison of Classification Algorithms on Large Real-World Problems”, Applied Artificial Intelligence, Vol 9, Issue 3, pp. 289-333, 1995
J. Zurada, S. Lonial, “Comparison of The Performance of Several Data Mining Methods for Bad Debt Recovery in The Healthcare Industry”, The Journal of Applied Business Research Vol 21, pp. 37-53, 2005
V. N. Rajavarman, S. P. Rajagopalan, “Comparison between Traditional data mining Techniques and Entropy-based Adaptive Genetic Algorithm for Learning Classification Rules”, International Journal of Soft Computing Vol 2 Issue 4, pp. 555-561, 2007
H. Sabzevari, M. Soleymani, E. Noorbakhsh, “A comparison between statistical and Data Mining methods for credit scoring in case of limited available data”, Eleventh Annual APRIA Conference, 2007.
T. R. Patil, S. S. Sherekar, “Performance Analysis of Navie Bayes and J48 Classification Algorithm for Data Classification”, International Journal of Computer Science and Applications, Vol. 6, No. 2, pp. 256-261, April 2013
A. K. Sharma, S. Sahni, “A Comparative Study of Classification Algorithms for Spam Email Data Analysis”, IJCSE, Vol. 3, No. 5, pp. 1890-1895, 2011
WEKA, http://www.cs.waikato.ac.nz/~ml/weka/ Last access: 10.04.2017
T. Hastie, R. Tibshirani, “Classification by pairwise coupling”, The Annals of Statistics, Vol. 26, No. 2, pp. 451-471, 1998
E. Frank, I. H. Witten, “Generating Accurate Rule Sets Without Global Optimization”, Fifteenth International Conference on Machine Learning, pp. 144-151, 1998.
J. Platt, “Machines using Sequential Minimal Optimization.”, Advances in Kernel Methods - Support Vector Learning, 1998.
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, “Improvements to Platt's SMO Algorithm for SVM Classifier Design.”, Neural Computation, 13(3):637-649, 2001
N. Landwehr, M. Hall, E. Frank, “Logistic model Trees”, Machine Learning, 59, pp. 161-205, 2005
M. Sumner, E. Frank, M. Hall, “Speeding up Logistic Model Tree Induction.”, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005
T. Hastie, R. Tibshirani, “Classification by Pairwise Coupling.”, Advances in Neural Information Processing Systems, 1998.
J. Weston, C. Watkins, “Multi-class support vector machines.”, In: Proc. ESANN, pp. 1–2, 1999
H. Qian, Y. Mao, W. Xiang, Z. Wang, “Recognition of human activities using SVM multi-class classifier”, Pattern Recognition Letters 31, pp. 100-111, 2010
I.H. Witten, E. Frank, “Data Mining-Practical Machine Learning Tools and Techniques”, Second Edition
O. Er, “Esnek Hesaplama ve BiyoBilişim Teknikleri ile Bir Klinik Karar Verme Simülatörünün Oluşturulması”, PhD Thesis, in Turkish
O. A. Er, A. C. Tanrikulu, A. Abakay, “An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease”, Computers & Electrıcal Engıneerıng, Volume: 38 Issue: 1, pp. 75-81, 2012.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
