A Neural Network Application to Classification of Health Status of HIV/AIDS Patients
Journal of Medical Systems
BIONET: an artificial neural network model for diagnosis of diseases
Pattern Recognition Letters
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
Journal of Medical Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Neural Network Approach in Diabetes Management by Insulin Administration
Journal of Medical Systems
Medical Diagnosis by the Virtual Physician
CBMS '99 Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network
Journal of Medical Systems
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
Journal of Medical Systems
Is Levenberg-Marquardt the Most Efficient Optimization Algorithm for Implementing Bundle Adjustment?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The application of neural networks in classification of epilepsy using EEG signals
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Systems Biology and Biomedical Technologies
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The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient's thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.