A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
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
Expert Systems with Applications: An International Journal
An expert system for optimising thyroid disease diagnosis
International Journal of Computational Science and Engineering
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
Hi-index | 12.05 |
In this paper, the automatic diagnosis system based on thyroid gland (ADSTG) method is introduced for diagnosis of thyroid disease. The structure of this ADSTG diagnosis system for thyroid diseases contains three stages. In first stage, the feature reduction is performed by using Principle Component Analysis (PCA) method. In second stage, the classification by using Least Square Support Vector Machine (LS-SVM) classifier. In third stage, the performance evaluation of this ADSTG method for diagnosis of thyroid disease is estimated by using classification accuracy, k-fold cross-validation, and confusion matrix methods respectively. The classification accuracy of this ADSTG diagnosis system for thyroid diseases was obtained about 97.67%.