Review on Various Clustering Algorithm on Healthcare based Cloud Analytics

It is common practice in the field of machine learning to make use of a clustering algorithm. The task of breaking up data on patients who have similar characteristics according to those qualities, which is essential for better disease prediction, is one that clustering algorithms perform exceptionally well. Several different clustering strategies have been devised so that the numerous data sets that are accessible in the field of healthcare can be analyzed. It is possible that the algorithms described in the literature operate more effectively with particular kinds of data sets than they do with other kinds of data sets, or that they will produce unsatisfactory results when applied to entirely different kinds of data sets. Cluster analysis was applied to either one or many data sets in a number of the studies that were conducted. It possible that they employed this strategy. On the other hand, not a lot of research has been done to determine the best size for clustering using a wide range of different kinds of data. The objective of this study is to investigate various clustering algorithms to find solution to the healthcare analytics problem.