Hybrid Thyroid Stage Prediction Models Combining Classification, Clustering and Ensemble Systems

  • Abstract
  • Keywords
  • References
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  • Abstract

    Early and correct detection of thyroid disease is very important for correct and timely treatment. The need to increase the accuracy of detecting and classifying thyroid disease poses a great challenge not only to the research community but also to healthcare industries. Usage of machine learning algorithms for thyroid disease classification is an area of research that is gaining popularity for the past few years. Automatic thyroid disease computer aided system for diagnosing the disease requires sophisticated and effective algorithms to perform classification in an accurate and time efficient manner. As a solution to this demand, hybrid models that combine clustering and classification algorithms along with ensemble technology are proposed. Four category of thyroid disease prediction system are proposed. They are Clustering + Classification models, Classification + Classification Models, Clustering + Clustering Models and Classification + Clustering Models. Two types of ensembles, namely, homogeneous and heterogeneous, are also considered and analyzed. Performance evaluation showed that the Classification + Classification model based on the combination of SVM and heterogeneous KNN + SVM classifier produce highest prediction accuracy.



  • Keywords

    Combining Clustering and Classification Algorithms, Expectation-Maximization Clustering, Hybrid Prediction Models, K-Means Clustering, KNN Classifier, SVM classifier, Thyroid Disease Diagnosis.

  • References

      [1] Benvenuto, F., Piana, M. and Campi, C. and Massone, A.M. (2018) A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction, The Astrophysical Journal, Vol. 853, No. 1, Pp. 90-105.

      [2] Chandra, B. (2009) Hybrid clustering algorithm, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, IEEE Pp. 1345-1348.

      [3] Dzelihodzic, A. and Donko, D. (2016) Comparison of Ensemble Classification Techniques and Single Classifiers Performance for Customer Credit Assessment, Modeling of Artificial Intelligence, Vol. 11, Issue 3, Pp. 140-150.

      [4] http://archive.ics.uci.edu/ml/datasets/Thyroid+Disease, Last Accessed During August, 2018.

      [5] https://en.wikipedia.org/wiki/Hybrid_algorithm, Last Accessed During August, 2018.

      [6] Hussein, S., Kandel, S., Bolan, C.W., Wallance, M.B. and Bagci, U. (2018) Supervised and Unsupervised Tumor Characterization in the Deep Learning Era, IEEE Transactions on Medical Imaging, Under Review, Pp. 1-11.

      [7] Pavya, K. and Srinivasan, B. (2017a) Feature Selection algorithms to improve thyroid disease diagnosis, IEEE International Conference on Innovations in Green Energy and Healthcare Technologies, Pp. 1-5.

      [8] Pavya, K. and Srinivasan, B. (2017b) Enhancing Filter Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset, International Journal of Advanced Research in Computer Science, Research Paper, Vol. 8, No. 9, Pp. 184-188.

      [9] Pavya, K. and Srinivasan, B. (2018a) Review of Literature on Filter and Wrapper Methods for Feature Selection, International Journal of Engineering Sciences & Research Technology, Vol. 7, Issue 1, Pp. 137-143.

      [10] Pavya, K. and Srinivasan, B. (2018b) Enhancing Wrapper Based Algorithms for Selecting Optimal Features from Thyroid Disease Dataset, Research Paper, International Journal of Computer Sciences and Engineering, Vol. 6, Issue 3, Pp. 7-13.

      [11] Roy, S.S., Ahmed, M. and Akhand, M.A.H. (2018) Noisy image classification using hybrid deep learning methods, Journal of Information and Communication Technology, Vol. 17, No. 2, Pp. 233–269.

      [12] Shen, H., Lin, Y., Tian, Q., Xu, K. and Jiao, J. (2018) A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification, International Journal of Remote Sensing, Vol. 39, Issue 11, Pp. 3705-3722.

      [13] Srinivasan, B. and Pavya, K. (2016a) A Study on Data Mining Prediction Techniques in Healthcare Sector, International Research Journal of Engineering and Technology, Vol. 3, Issue 3, Pp. 552-556.

      [14] Srinivasan, B. and Pavya, K. (2016b) Diagnosis of Thyroid Disease Using Data Mining Techniques: A Study, International Research Journal of Engineering and Technology, Vol. 3, Issue: 11, Pp. 1191-1194.

      [15] Srinivasan, B. and Pavya, K. (2016c) A Comparative Study on Classification Algorithms in Data Mining, International Journal of Innovative Science, Engineering & Technology, Vol. 3, Issue 3, Pp. 415-418.

      Tsai, C.F. and Chen, M.L. (2010) Credit rating by hybrid machine learning techniques, Elsevier Journal of Applied Soft Computing, Vol. 10, Pp. 374-380.




Article ID: 20565
DOI: 10.14419/ijet.v7i4.7.20565

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