Artificial neural network system for thyroid diagnosis
Author
Source
Journal of University of Babylon for Engineering Sciences
Issue
Vol. 25, Issue 2 (30 Apr. 2017), pp.518-528, 11 p.
Publisher
Publication Date
2017-04-30
Country of Publication
Iraq
No. of Pages
11
Main Subjects
Abstract EN
Thyroid disease is one of major causes of severe medical problems for human beings.
Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients.
This paper focuses on using Artificial Neural Network (ANN) as a significant technique of artificial intelligence to diagnose thyroid diseases.
The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN.
All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system.
A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process.
The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses.
The system is simulated via MATLAB software to evaluate its performance.
American Psychological Association (APA)
Hamid, Mazin Abd al-Rasul. 2017. Artificial neural network system for thyroid diagnosis. Journal of University of Babylon for Engineering Sciences،Vol. 25, no. 2, pp.518-528.
https://search.emarefa.net/detail/BIM-923321
Modern Language Association (MLA)
Hamid, Mazin Abd al-Rasul. Artificial neural network system for thyroid diagnosis. Journal of University of Babylon for Engineering Sciences Vol. 25, no. 2 (2017), pp.518-528.
https://search.emarefa.net/detail/BIM-923321
American Medical Association (AMA)
Hamid, Mazin Abd al-Rasul. Artificial neural network system for thyroid diagnosis. Journal of University of Babylon for Engineering Sciences. 2017. Vol. 25, no. 2, pp.518-528.
https://search.emarefa.net/detail/BIM-923321
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 527-528
Record ID
BIM-923321