Methodologies of Data Mining in Healthcare: A Inspection

A. R. Pon Periasamy, S. Mohan
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of
more » ... ata mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare. Selection The data is selected according to some criteria in this stage. For example, a bicycle owns by all those people, we can determine subsets of data in this way. Preprocessing This stage removes that information which is not necessary for example while doing pregnancy test it is not necessary to note the sex of a patient. It is also known as data cleansing stage. Transformation This stage transformed only those data which are useful in a particular research for example only data related to a particular demography is useful in market research. Data mining Data mining is a stage knowledge discovery process. This stage is useful for extracting the meaningful patterns from data. Interpretation and evaluation The meaningful patterns which the system identified are interpreted into knowledge in this stage. This knowledge may be then useful for making useful decisions.
doi:10.23956/ijarcsse/v7i3/0135 fatcat:ce53i4dmw5cmrpzfz7fwip5sga