Unsupervised clustering methods for medical data: An application to thyroid gland data


ARTIFICAIL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003, vol.2714, pp.695-701, 2003 (SCI-Expanded) identifier identifier


The purpose of this paper is to examine the unsupervised clustering methods on medical data. Neural networks and statistical methods can be used to develop an accurate automatic diagnostic system. Self-Organizing Feature map as a Neural Network model and K-means as a statistical model are tested to predict a well defined class. To test the diagnostic system, thyroid gland data is used for the application. As a result of clustering algorithms, patients are classified normal, hyperthyroid function and hypothyroid function.