Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

Authors(3) :-Umar Sidiq, Dr. Syed Mutahar Aaqib, Dr. Rafi Ahmad Khan

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 131-136
Manuscript Number : CSEIT195119
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

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
Journal URL : http://ijsrcseit.com/CSEIT195119

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