Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

Authors

  • Umar Sidiq  Research Scholar, Department of Computer Science, Mewar University, Rajasthan India
  • Dr. Syed Mutahar Aaqib  Assistant Professor, Department of Computer Science, Amar Singh College, Srinagar, Jammu and Kashmir, India
  • Dr. Rafi Ahmad Khan  Assistant Professor, University of Kashmir Srinagar, Jammu and Kashmir, India

DOI:

https://doi.org/10.32628/CSEIT195119

Keywords:

Thyroid disease, K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naive Bayes.

Abstract

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naive bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-01-30

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Umar Sidiq, Dr. Syed Mutahar Aaqib, Dr. Rafi Ahmad Khan, " Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.131-136, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT195119