Data mining: concepts and techniques
Data mining: concepts and techniques
Journal of Biomedical Informatics
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
HIS'12 Proceedings of the First international conference on Health Information Science
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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.