Comparative Study on Classification of Thyroid Diseases
Citation
Suman Pandey, Deepak Kumar Gour, Vivek Sharma"Comparative Study on Classification of Thyroid Diseases", International Journal of Engineering Trends and Technology (IJETT), V28(9),457-460 October 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Diagnosis of health conditions is a very
challenging task in field of medical science. Diagnoses of
health conditions are based on the physician experience. Data
mining technique plays role to diagnosis of diseases of
patients. Classification is one of the important data mining
applications for classification of data. In this work, our main
purpose is to propose a robust classifier and compared with
other existing classifier which is developed by various
authors. We have developed classifications models and its
ensemble model for classification of thyroid data. Feature
selection is also applied to improve the classification
accuracy and increases performance. This paper has used an
ensemble of C4.5 and Random Forest which improve
classification accuracy of model compared to individual
models as well as existing techniques.
References
[1] Arun K. Pujari, ?Data Mining Techniques, 4th edition, Universities
Press (India) Private Limited, 2001.
[2] Farhad Soleimanian Gharehchopogh, Maryam Molanyand and Freshte
Dabaghchi, ?Using artificial neural network in diagnosis of thyroid
deceases A case Study, International Journal on Computational
Sciences & Applications (IJCSA) Vol. 3, No.4, pp. 49-61, 2013 .
[3] M. R. Nazari Kousarrizi, F.Seiti, and M. Teshnehlab, ?An Experimental
Comparative Study on Thyroid Disease Diagnosis Based on Feature
Subset Selection and classification, International Journal of Electrical
& Computer Sciences IJECS-IJENS Vol: 12 No: 01, pp. 13-19,
February 2012.
[4] Ali Keles, Ayturk Keles ?ESTDD: Expert system for thyroid diseases
diagnosis, Expert system with Apllications, 34,242-246,200.
[5] Esin Dogantekin,Asif Dogantekin,Derya Avci. ?An automatic diagnosis
system based on thyroid gland:ADSTG,Expert system with
applications, 37(9) ,6368-6372, September 2010.
[6] Esin Dogantekin,Asif Dogantekin,Derya Avci. ?An expert system based
on generalized discriminant analysis and wavelet support vector
machine for diagnosis of thyroid diseases, Expert system with
Application, 38((1), 146-150, January 2011
[7] Shivanee Pandey, Rohit Miri, S.R. Tandan, ?Diagnosis and
Classification of Hypothyroid Disease Using Data Mining Techniques,
International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181 Vol. 2 Issue 6, June 2013.
[8] S.Yasodha and P. S.Prakash, ?Data Mining Classification Technique
for Talent Management using SVM, International Conference on
Computing, Electronics and Electrical Technologies (ICCEET), 978-1-
4673-02l0-4/12, pp. 959-963, 2012.
[9] D.Kerana Hanirex and DR.K.P.Kaliyamurthie, ?Multi-classification
approach for detecting Thyroid attacks”, Int J Pharm Bio Sci, pp. - 4(3):
1246 – 1251, July 2013.
[10] E. Zoulias,P.A. Asvestas, G.K. Matsopoulos, N. Uzunoglu, S. Tseleni-
Balafouta, H. Gakiopoulou, “A data mining approach for classifying
FNA thyroid data, School of Electrical and Computer Engineering,
National Technical University of Athens, Greece. Department of
Pathology, Medical School, University of Athens, Greece.
[11] Alfonso Bastias, Ph.D., Eleonora Horvath, M.D., Felipe Baesler, Ph.D.,
and Claudio Silva, M.D., ?Predictive model based on neural networks to
assist the diagnosis of malignancy of thyroid nodules, Proceedings of
the 41st International Conference on Computers & Industrial
Engineering.
[12] Anurag Upadhayay (M. Tech Scholar), Suneet Shukla, Sudsanshu
Kumar, ?Empirical Comparison by data mining Classification
algorithms (C 4.5 & C 5.0) for thyroid cancer data set, International
Journal of Computer Science & Communication Networks,Vol 3(1),
pp.- 64-68.
[13] R., Parimala, ?A studt of spam E-mail classification using feature
selection package, Global General of computer science and technology,
vol. 11, ISSN 0975-4175, 2011.
[14] H.S.Hota, ?Diagnosis of Breast Cancer Using Intelligent Techniques,
International Journal of Emerging Science and Engineering (IJESE)
ISSN: 2319–6378, Volume-1, Issue 3, .January 2013.
[15] UCI Repository of Machine Learning Databases, University of
California at Irvine, Department of Computer Science. Available:
http://www.ics.uci.edu/~mlearn/databases/thyroiddisease/
newthyroid.data (Accessed: 12 Jan 2015).
[16] Wang, J., ?Data Mining: opportunities and challenge, Idea Group, USA,
2003.
[17] Han, J. & Micheline, K., ?Data Mining: Concept and Techniques,
Morgan Kaufmann publisher, 2006.
[18] Ms. Nikita Singh, Mrs. Alka Jindal ?A segmentation method and
classification of diagnosis for thyroid nodules , IOSR Journal of
Computer Engineering (IOSRJCE) ISSN : 2278-0661 Volume 1, Issue 6,
PP 22-27 , July-Aug 2012.
[19] Sheetal Gaikwad and Nitin Pise ?An Experimental Stydy on Hypothyroid
Using Rotation Forest, International Journal of Data Mining &
Knowledge Management Process (IJDKP) Vol.4, No.6, November
2014.
[20] Gurmeet Kaur, Er.Brahmaleen Kaur Sidhu, ?Proposing Efficient Neural
Network Training Model for Thyroid Disease Diagnosis, International
Journal for Technological Research in Engineering Volume 1, Issue 11,
July-2014.
[21] Bruno Fernandes Chimieski1, Rubem Dutra Ribeiro Fagundes,
?Association and Classification Data Mining Algorithms Comparison
over Medical Datasets, J. Health Inform. Abril-Junho; 5(2): 44-5,
2013.
[22] Electronic Decision Support for Austraila’s Health Sector, National
electronic decision support taskforce, 2002.
[23] http://www.cs.waikato.ac.nz/ml/weka/
Keywords
Thyroid diseases, Feature Selection, C4.5,
Random Forest, Multilayer Perceptron (MLP), Bayesian Net.