Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6335
Title: Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering
Authors: Übeyli, Elif Derya
Doğdu, Erdoğan
Keywords: Clustering
k-Means clustering
Erythemato-squamous diseases
Classification accuracy
Issue Date: 2010
Publisher: Springer
Abstract: A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.
URI: https://doi.org/10.1007/s10916-008-9229-6
https://hdl.handle.net/20.500.11851/6335
ISSN: 0148-5598
1573-689X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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