A. K. Shrivas, Pallavi Ambastha


Now a day’s people are facing lots of problem related to health. Diseases are also increasing due to increase number of populations. The survey helps to identify how the data mining techniques predict the thyroid disorder at earlier stage. Classification techniques play very important role to identify the disease in medical data. In this paper, the main objective is to classify the data as thyroid or non-thyroid and improve the classification accuracy. We have proposed robust ensemble model using various classification techniques like random forest, Naïve Bayes and K-Nearest Neighbors (K-NN). The proposed model gives better classification accuracy as 93.55%.We have also applied the feature optimization technique that is optimized selection to eliminate the irrelevant feature from data set and computationally improve the performance of model .The proposed model achieved better classification technique as  97.61% of accuracy with reduced 3 feature subset.


Thyroid, Decision tree, Classification, Ensemble Model, Optimization Selection.

Full Text:



S. Gaikwad and N. Pise, An Experimental study on Hypothyroid using Rotation Forest ,International Journal of Data Mining & Knowledge Management Process(IJDKP),vol.4(.6),pp.36-37, 2014.

A. Upadhyay,S. Shukla and S. kumar, Empirical Comparison by data mining classification algorithms(C4.5 & C5.0) for thyroid cancer data set, International Journal of Computer Science & Communication Networks, vol. 3(1,),pp.64-68.

N. Sigh and A. Jindal, A Segmentation Method and Comparison of Classification Methods for Thyroid Ultrasound Images, International Journal of Computer Applications, Vol. 50(11), 2012.

M. C. Frates, C. Benson, J. Charbonneau and S. Edmund ., “Management of Thyroid Nodules Detected at US: Society of Radiologists in US consensus”, Conference statement management of thyroid nodules detected at US, Vol. 237(3), 2005.

Thyroid Data Set [online]. Available: s/Thyroid Disease, (Browsing Dec 2016).

H. Jiawei, K. Micheline, P. Jian, Data Mining Concepts and Techniques, Morgan Kaufmann, 2006.

D. Kerana Hanirex and K. P. Kaliyamurthie , Multi-Classification Approach For Detecting Thyroid Attacks”,International journal of Pharma and Bio sciences, vol.4(3),pp.1246-1251,2013.

D. Lavanya & .K. Usha Rani, Performance Evalution of Decision Tree Classifiers on Medical Datasets, International Journal of Computer Application, vol.26(4),2011.

R. Parimala and R. Nallaswamy, A Study of Spam e-mail Classification using Feature Selection Package. Global Journal of Computer Science and Technology, Vol. 11, 2011.

K. J. Cios, W. W. Pedrycz , and R. W. Swiniarski , Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers, 3rd ed., 1998.

M. Pal, Ensemble Learning with Decision Tree for Remote Sensing Classification.World Academy of Science, Engineering and Technology. 36: 258-260, 2007.

S. Pandey,A. Tiwari , A. K. Shrivas and V. Sharma, Thyroid Classification using Ensemble Model with Feature Selection, International Journal of Computer Science and Information Technologies, Vol. 6 (3) , pp. 2395-2398,2015.

K. Geetha and Baboo C. S. Santosh, Efficient Thyroid Disease Classification Using Differential Evolution With SVM, Journal Of Theoretical And Applied Information Technology , Vol.88(3),410-420,2016.

K. Rajam, A Survey on Diagnosis of Thyroid Disease Using Data Mining Techniques, International Journal of Computer Science and Mobile Computing, Vol. 5( 5), pp.354–358, 2016.

Source : Help File of Rapid Miner (Browsing date: Feb. 2016).


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.