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

Authors:
Songül Albayrak
Affiliations:
Computer Engineering Department, Yildiz Technical University, Istanbul, Turkiye
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

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.