Artificial Neural Network System for Thyroid Diagnosis | ||
| Journal of University of Babylon | ||
| Article 1, Volume 25, Issue 2, April 2017, Pages 518-528 | ||
| Author | ||
| Mazin Abdulrasool Hameed | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| artificial neural network; Neuron; Back Propagation; Multilayer feedforward; Thyroid disease; Classification rate; diagnosis | ||
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