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
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.