Thyroid prediction using ensemble data mining techniques

  • Dhyan Chandra YadavEmail author
  • Saurabh Pal
Original Research


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.


Data mining meta classifier algorithms Boosting Bagging Stacking Voting algorithms 



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.


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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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