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Thyroid prediction using ensemble data mining techniques

  • Dhyan Chandra YadavEmail author
  • Saurabh Pal
Original Research
  • 5 Downloads

Abstract

Data mining algorithms provide easy way to solve problem in medical data analysis. Data mining supports in complex data analysis to identify each issue in dataset. Now-a-days every person suffers for a good heath. The life style of every person is very fast so it is very difficult to maintain his health. Every person cannot easily maintain the hormone system in the body. Nowadays hormone disturbance are major issue in ladies. The major issues behind thyroids are hormonal disturbance. Initially we do not care the symptom of thyroids. If we have some knowledge about thyroid symptom then prior of major problem we protect his life. The symptoms of thyroid are very similar so we easily can not eliminate for identification. Data mining provide major help in thyroid dataset with different algorithms for classification, clustering, association etc. We use different types of machine learning meta classifier algorithms for thyroid dataset classification. In this analysis we analyze thyroid large and complex dataset by data mining meta classifier algorithm: Boosting, Bagging, Stacking and Voting with new ensemble model and con comparing classification accuracy, sensitivity and specificity. We easily classify thyroid dataset in different class level. Thyroid is a very common disease found in the human body, which is related to human diet and daily living with the help of classification algorithms, they can be avoided by studying various types of functions in thyroid, with the help of classification, after the disease consisting of experienced doctor walking expert system can be developed.

Keywords

Data mining meta classifier algorithms Boosting Bagging Stacking Voting algorithms 

Notes

Acknowledgements

The author is grateful to Veer Bahadur Singh Purvanchal University Jaunpur, Uttar Pradesh, for providing financial support to work as Post Doctoral Research Fellowship.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. 1.
  2. 2.
    Verma AK, Pal S, Kumar S (2019) Classification of skin disease using ensemble data mining techniques. Asian Pac J Cancer Prev 20(6):1887–1894CrossRefGoogle Scholar
  3. 3.
    Yadav DC, Pal S (2019) To generate an ensemble model for women thyroid prediction using data mining techniques. Asian Pac J Cancer Prev 20(4):1275–1281CrossRefGoogle Scholar
  4. 4.
    Ahmad W, Huang L, Ahmad A, Shah F, Iqbal A, Saeed A (2017) Thyroid diseases forecasting using a hybrid decision support system based on ANFIS, k-NN and information gain method. J Appl Environ Biol Sci 7(10):78–85Google Scholar
  5. 5.
    Chaurasia V, Pal S, Tiwari BB (2018) Prediction of benign and malignant breast cancer using data mining techniques. J Algorithm Comput Technol 12(2):119–126CrossRefGoogle Scholar
  6. 6.
    Shankar K, Lakshmanaprabu SK, Gupta D, Maseleno A, de Albuquerque VH (2018) Optimal feature-based multi-kernel SVM approach for thyroid disease classification. J Supercomput.  https://doi.org/10.1007/s11227-018-2469-4 CrossRefGoogle Scholar
  7. 7.
    Sumathi A, Nithya G, Meganathan S (2018) Classification of thyroid disease using data mining techniques. Int J Pure Appl Math 119(12):13881–13890Google Scholar
  8. 8.
    Ali Z, Shahzad W (2017) Comparative analysis and survey of ant colony optimization based rule miners (IJACSA). Int J Adv Comput Sci Appl 8(1):49–60Google Scholar
  9. 9.
    Sudhan M, Kannan M, Sinha D (2017) Hybrid disease diagnosis using multiobjective optimization with evolutionary parameter optimization Hindawi. J Healthc Eng Article ID 5907264Google Scholar
  10. 10.
    Pravin SR, Jafar OA (2017) Prediction of skin disease using data mining techniques. IJARCCE 6(7):313–318Google Scholar
  11. 11.
    Wang J, Li S, Song W, Qin H, Zhang B, Hao A (2018) Learning from weakly-labeled clinical data for automatic thyroid nodule classification in ultrasound images. In: 2018 25th IEEE international conference on image processing (ICIP), IEEE, pp 3114–3118Google Scholar
  12. 12.
    Sivasakthivel A, Shrivakshan GT (2017) A comparative study of diagnosing thyroid diseases using classification algorithm. Int J Adv Res Comput Sci Softw Eng 7(8):181–184CrossRefGoogle Scholar
  13. 13.
    IoniŃă I, IoniŃă L (2016) Prediction of thyroid disease using data mining techniques. Brain Broad Res Artif Intell Neurosci 7(3):115–127Google Scholar
  14. 14.
    Rathi M, Pareek V (2016) Disease prediction tool: an integrated hybrid data mining approach for healthcare. IRACST Int J Comput Sci Inf Technol Secur (IJCSITS) 6(6):32–40Google Scholar
  15. 15.
    Geetha K, Baboo CSS (2016) Efficient thyroid disease classification using differential evolution with SVM. J Theoret Appl Inf Technol 88(3):410–420Google Scholar
  16. 16.
    Parikh KS, Shah TP, Kota R, Vora R (2015) Diagnosing common skin diseases using soft computing techniques. Int J Bio-Sci Bio-Technol 7(6):275–286CrossRefGoogle Scholar
  17. 17.
    Surekha S, Suma GJ (2015) Comparison of feature selection techniques for Thyroid disease. In: ICICMT’2015, AbuDhabi (UAE), pp 20–26Google Scholar
  18. 18.
    Razia S, Narasingarao MR, Sridhar GR (2015) Decision support system for prediction of thyroid disease—a comparison of multilayer perceptron neural network and radial function neural network. J Theoret Appl Inf Technol 80(3):544–551Google Scholar
  19. 19.
    Gaikwad S, Pise N (2014) An experimental study on hypothyroid using rotation forest. Int J Data Min Knowl Manag Process (IJDKP) 4(6):31.  https://doi.org/10.5121/ijdkp.2014.4603 CrossRefGoogle Scholar
  20. 20.
    Akbaş A, Turhal U, Babur S, Avci C (2013) Performance improvement with combining multiple approaches to diagnosis of thyroid cancer. Scrp J Eng 5:264–267Google Scholar
  21. 21.
    Pandey S, Miri R, Tandan SR (2013) Diagnosis and classification of hypothyroid disease using data mining technique. Int J Eng Res Technol (IJERT) 2(6):3188–3193Google Scholar
  22. 22.
    Chaurasia V, Pal S, Tiwari BB (2018) Chronic kidney disease: a predictive model using decision tree. Int J Eng Res Technol 9(11):1781–1794Google Scholar
  23. 23.

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.VBS Purvanchal University (VBSPU)JaunpurIndia
  2. 2.Department of MCAVBS Purvanchal University (VBSPU)JaunpurIndia

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