Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Unsupervised clustering methods for medical data: an application to thyroid gland data
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Multi-class support vector machine for classification of the ultrasonic images of supraspinatus
Expert Systems with Applications: An International Journal
Novel swarm optimization for mining classification rules on thyroid gland data
Information Sciences: an International Journal
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
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The primary role of the thyroid gland is to help regulation of the body's metabolism. The correct diagnosis of thyroid dysfunctions is very important and early diagnosis is the key factor in its successful treatment. In this article, we used four different kinds of classifiers, namely Bayesian, k-NN, k-Means and 2-D SOM to classify the thyroid gland data set. The robustness of classifiers with regard to sampling variations is examined using a cross validation method and the performance of classifiers in medical diagnostic is visualized by using cobweb representation. The cobweb representation is the original contribution of this work to visualize the classifiers performance when the data have more than two classes. This representation is a newly used method to visualize the classifiers performance in medical diagnosis.