An Ensemble Approach for Classification of Thyroid Using Machine Learning

  • Bhavna Dharamkar
  • Praneet Saurabh
  • Ritu Prasad
  • Pradeep Mewada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


Medical diagnosis and extracting patterns that can be converted into useful knowledge is a challenging work for researchers. Medical records are based on real-time data that have high dimensionality which makes task of extracting patterns even more complex. Prediction of various diseases often suffers due to large dimensionality that includes thyroid, diabetes, cancer, etc. Data mining is a process of finding out information and constructing a knowledge base from a huge amount of data, which remains useful. Data mining achieves these tasks involving classification, clustering, regression, and prediction. Reduction of dimensionality with the motive of knowledge discovery is an essential aspect of data mining as it helps in prediction/determination for various data analytics filed like medical diagnosis, business modeling, and government data analysis. This paper presents cooperative method for classification of thyroid using machine learning fusing C4.5 and random forest classification technique (CCTML). Experimental results are compared with other conventional techniques and the results obtained through CCTML reported better classification accuracy and outperform conventional methods.


Thyroid Classification Feature selection C4.5 Random forest Multilayer perceptron Bayesian net 


  1. 1.
    Zadeh, H.G., Janianpour, S., Haddadnia, J.: Recognition and classification of the cancer cells by using image processing and lab view. Int. J. Comput. Theor. Eng. (2009)Google Scholar
  2. 2.
    Leung, C.C., Chan, F.H.Y., Lam, K.Y., Kwok, P.C.K., Chen, W.F.: Thyroid cancer cells boundary location by a fuzzy edge detection method. In: Proceedings of the 15th international conference on pattern recognition (2000)Google Scholar
  3. 3.
    Hudli, S.A., Hudli, A.V., Hudli, A.A.: Application of data mining to candidate screening. In: 2012 IEEE international conference on advanced communication control and computing technologies (ICACCCT), pp. 287–290. IEEE (2012)Google Scholar
  4. 4.
    Munla, N., Khalil, M., Shahin, A., Mourad, A.: Driver stress level detection using HRV analysis. In: 2015 international conference on advances in biomedical engineering (ICABME), pp. 61–64. IEEE (2015)Google Scholar
  5. 5.
    Shariati, S., Haghighi, M.M.: Comparison of Anfis neural network with several other ANNs and support vector machine for diagnosing hepatitis and thyroid diseases. In: Proceedings of IEEE IACSIT 2010, pp. 596–599 (2010)Google Scholar
  6. 6.
    Jagdeeshkannan, R., Aarthi, G., Akshaya, L., Ravy, K.: An approach for automated detection and classification of thyroid cancer cells. In: ICT and critical infrastructure, proceedings of the 48th annual convention of computer society of India, 2, pp. 379–389 (2014)Google Scholar
  7. 7.
    Chandel, K., Kunwar, V., Sabitha, S.: A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques. CSI Trans. ICT 4(2–4), 313–319 (2016)CrossRefGoogle Scholar
  8. 8.
    Amina, B., Parkavi, A.: Prediction of thyroid disease using data mining techniques. In: 5th international conference on advanced computing and communication systems (ICACCS), pp. 342–345 (2019)Google Scholar
  9. 9.
    Dogantekin, E., Dogantekin, A., Avci, D.: An automatic diagnosis system based on thyroid gland: ADSTG. Expert Syst. Appl. 37(9), 6368–6372 (2010)CrossRefGoogle Scholar
  10. 10.
    Cetişli, B.: Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Syst. Appl. 37(8), 6093–6101 (2010)CrossRefGoogle Scholar
  11. 11.
    Azar, A.T., Hassanien, A.E., Kim, T.: Expert system based on neural-fuzzy rules for thyroid diseases diagnosis, pp. 94–105. Springer (2012)Google Scholar
  12. 12.
    Omiotek, Z., Burda, A., Wójcik, W.: Application of selected classification methods for detection of Hashimoto’s thyroiditis on the basis of ultrasound images, computational intelligence, medicine and biology, pp. 23–37. Springer (2015)Google Scholar
  13. 13.
    Sahu, S., Saurabh, P., Rai, S.: An enhancement in clustering for sequential pattern mining through neural algorithm using web logs. In: International conference on computational intelligence and communication networks, pp. 758–764 (2015)Google Scholar
  14. 14.
    Saxena, M., Saurabh, P., Verma, B.: A new hashing scheme to overcome the problem of overloading of articles in Usenet, pp. 967–975. Springer, AISC (2012)Google Scholar
  15. 15.
    Mishra, B.K., Saurabh, P., Verma, B.: A novel approach to classify high dimensional datasets using supervised manifold learning, pp. 22–30. Springer, CCIS (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Bhavna Dharamkar
    • 1
  • Praneet Saurabh
    • 2
  • Ritu Prasad
    • 1
  • Pradeep Mewada
    • 1
  1. 1.Technocrats Institute of Technology AdvanceBhopalIndia
  2. 2.Mody University of Science and TechnologyLakshmangarhIndia

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