Automatic detection of erythemato-squamous diseases using k-means clustering.
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.
Ubeyli
Elif Derya
ED
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomi ve Teknoloji Universitesi, 06530 Söğütözü, Ankara, Turkey. edubeyli@etu.edu.tr
Doğdu
Erdoğan
E
D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining; D012628:Dermatitis, Seborrheic / Q000145:classification; D006801:Humans; D012680:Sensitivity and Specificity; D017444:Skin Diseases, Papulosquamous / Q000145:classification*