Visualization and analysis of classifiers performance in multi-class medical data

Diri B., Albayrak S.

EXPERT SYSTEMS WITH APPLICATIONS, vol.34, pp.628-634, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34
  • Publication Date: 2008
  • Doi Number: 10.1016/j.eswa.2006.10.016
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.628-634
  • Keywords: bayesian, k-NN, k-means, 2-D SOM, cross validation, confusion matrix, ROC analysis, cobweb representation, thyroid gland data, medical diagnosis
  • Yıldız Technical University Affiliated: Yes


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. (c) 2006 Elsevier Ltd. All rights reserved.