|International Journal of Computer Applications
|Foundation of Computer Science (FCS), NY, USA
|Volume 131 - Number 12
|Year of Publication: 2015
|Authors: Rupali Hande, Vishal Bulchandani, Hitesh Batreja, Karan Jaisinghani, Sagar Nagwan
Rupali Hande, Vishal Bulchandani, Hitesh Batreja, Karan Jaisinghani, Sagar Nagwan . Mining Medical Data for Identifying Frequently Occuring Diseases by using Apriori Algorithm. International Journal of Computer Applications. 131, 12 ( December 2015), 18-20. DOI=10.5120/ijca2015907260
Data mining is a process of analyzing data from various perspectives and trims it into useful information. The data can be transformed into knowledge for future use and history patterns. Data mining has a vital role in the domain of information technology. There is a lot of complex data being generated by the health care industry. It includes the details of various patients, hospitals, diagnosis techniques, diseases, etc. The data mining methods prove to be useful for making decisions related to the curing of diseases. The information is hidden because the huge data gathered by the health care industry is not mined. Thus effective decisions cannot be made. The information gained after data mining can be used by doctors and health care administrators for improving the quality of service. In this paper, identification of frequent diseases in a specific location is done using Apriori algorithm. Association rules are applied to extract patterns that occur frequently within a data set. For extracting the results, WEKA tool is used.