Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fingerprint Classification with Combinations of Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Expert Systems with Applications: An International Journal
ESTDD: Expert system for thyroid diseases diagnosis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis
Journal of Medical Systems
An expert system for optimising thyroid disease diagnosis
International Journal of Computational Science and Engineering
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Optimizing the modified fuzzy ant-miner for efficient medical diagnosis
Applied Intelligence
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
International Journal of Systems Biology and Biomedical Technologies
A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems
Journal of Medical Systems
Hi-index | 12.05 |
Nowadays, there are many persons, which suffer from thyroid diseases. Therefore, the correct diagnosis of these diseases are very important topic. In this study, a Generalized Discriminant Analysis and Wavelet Support Vector Machine System (GDA_WSVM) method for diagnosis of thyroid diseases is presented. This proposed system includes three phases. These are feature extraction - feature reduction phase, classification phase, and test of GDA_WSVM for correct diagnosis of thyroid diseases phase, respectively. The correct diagnosis performance of this GDA_WSVM expert system for diagnosis of thyroid diseases is estimated by using classification accuracy and confusion matrix methods, respectively. The classification accuracy of this expert system for diagnosis of thyroid diseases was obtained about 91.86%.