Advertisement

An Empirical Model for Thyroid Disease Diagnosis Using Data Mining Techniques

  • Umar SidiqEmail author
  • Syed Mutahar Aaqib
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

Utilization of data mining in healthcare sectors showing great role in effectiveness of treatment, healthcare administration, finding of fraud and abuse and customer relationship management but besides that it is also used for diagnosis of diseases. In this work, our focus is on diagnosis of thyroid diseases by using three classification models like K-Nearest Neighbor (K-NN), Decision Tree and Naïve bayes based on certain clinical thyroid attributes like Age, Gender, TSH, T3 and T4. The entire research work is to be conducted with RapidMiner version 8.1, an open source tool under Windows 10 environment. The Experimental study shows that decision tree outperformed over other models.

Keywords

Thyroid disease K-nearest neighbor Decision tree Naïve Bayes 

Notes

Acknowledgment

There is no conflict of interest. We used our own data.

References

  1. 1.
    Sehgal, M.S.B., Gondal, I.: K-ranked covariance based missing values estimation for microarray data classification. IEEE (2004)Google Scholar
  2. 2.
    Bonner, A.: Comparison of discrimination methods for peptide classification in tandem mass spectrometry. IEEE (2004)Google Scholar
  3. 3.
  4. 4.
    Shen, X., Lin, Y.: Gene expression data classification using SVM–KNN classifier. IEEE (2004)Google Scholar
  5. 5.
    Xia, C., Hsu, W.: BORDER: efficient computation of boundary points. IEEE (2006)Google Scholar
  6. 6.
    Kodaz, H., et al.: Medical application of information gain based artificial immune recognition system (AIRS): diagnosis of thyroid disease. Expert Syst. Appl. 36(2), 3086–3092 (2009)CrossRefGoogle Scholar
  7. 7.
    Temurtas, F.: A comparative study on thyroid disease diagnosis using neural networks. Expert Syst. Appl. 36, 944–949 (2009)CrossRefGoogle Scholar
  8. 8.
    Ozyılmaz, L., Yıldırım, T.: Diagnosis of thyroid disease using artificial neural network methods. In: Proceedings of ICONIP 2002 9th International Conference on Neural Information Processing, pp. 2033–2036. Orchid Country Club, Singapore (2002)Google Scholar
  9. 9.
    Polat, K., Sahan, S., Gunes, S.: A novel hybrid method based on artificial immune recognition system (AIRS) with fuzzy weighted preprocessing for thyroid disease diagnosis. Expert Syst. Appl. 32, 1141–1147 (2007)CrossRefGoogle Scholar
  10. 10.
    http://en.wikipedia.org. Accessed on 24 Dec
  11. 11.
    Apte, C., Weiss, S.M.: Data Mining with Decision Trees and Decision Rules. T. J. Watson Center (1997). http://www.research.ibm.com/dar/papers/pdf/fgcsapteweissue_with_cover.pdf
  12. 12.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, San Francisco, pp. 338–345 (1995)Google Scholar
  13. 13.
    Saravana Kumar, K., Manicka Chezian, R.: Support vector machine and k-nearest neighbor based analysis for the prediction of hypothyroid. Int. J. Pharma Bio Sci. 2(5), 447–453 (2014)Google Scholar
  14. 14.
    Keles, A., Keles, A.: ESTDD: Expert system for thyroid diseases diagnosis. Expert Syst. Appl. 34(1), 242–246 (2008)CrossRefGoogle Scholar
  15. 15.
    Dogantekin, E., Dogantekin, A., Avci, D.: An expert system based on generalized discriminant analysis and wavelet support vector machine for diagnosis of thyroid diseases. Expert Syst. Appl. 38(1), 146–150 (2011)CrossRefGoogle Scholar
  16. 16.
    Gopinath, M.P.: Comparative study on classification algorithm for thyroid data set. Int. J. Pure Appl. Math. 117(7), 53–63 (2017)Google Scholar
  17. 17.
    Sidiq, U., et al.: Diagnosis of various thyroid ailments using data mining classification techniques. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. (IJSRCSEIT) 5(1), 131–136 (2019). ISSN 2456-3307Google Scholar
  18. 18.
    Roychowdhury, S.: DREAM: diabetic retinopathy analysis using machine learning. IEEE (2014)Google Scholar
  19. 19.
    Chetty, N., Vaisla, K.S., Patil, N.: An improved method for disease prediction using fuzzy approach. IEEE (2015)Google Scholar
  20. 20.
    Jacob, J., et al.: Diagnosis of liver disease using machine learning techniques. IRJET 05(04), 4011–4014 (2018)Google Scholar
  21. 21.
    Srinivasan, B., Pavya, K.: Diagnosis of thyroid disease using data mining techniques: a study. Int. Res. J. Eng. Technol. 3(11), 1191–1194 (2016)Google Scholar
  22. 22.
    Chandel, K., Kunwar, V., Sabitha, S., Choudhury, T., Mukherjee, 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

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and System StudiesMewar UniversityChittorgarhIndia
  2. 2.Department of Computer ScienceAmar Singh CollegeSrinagarIndia

Personalised recommendations