On the Use of Clustering Algorithms in Medical Domain
the exponential growth of data known nowadays forces health managers to use automatic processing solutions instead of manual ones. In this optics, the present paper aims to evaluate the unsupervised clustering methods applied on medical data, especially on nasopharyngeal carcinoma data. This technique allows to the clinicians to predict the development of cancer of a sick person based on his lifestyle, his environment,etc. For this end, different clustering algorithms were tested on medical data and evaluated using six metrics. The results show that density based algorithms provide an efficient solution for medical and
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