Open AccessDe-Identified Personal Health Care System Using Hadoop
Author(s) -
Dasari Madhavi,
Bendi Venkata Ramana
Publication year - 2015
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i6.pp1492-1499
Subject(s) - health care , big data , identification (biology) , computer science , medical record , personally identifiable information , database , data mining , medicine , computer security , botany , radiology , biology , economics , economic growth
Hadoop technology plays a vital role in improving the quality of healthcare by delivering right information to right people at right time and reduces its cost and time. Most properly health care functions like admission, discharge, and transfer patient data maintained in Computer based Patient Records (CPR), Personal Health Information (PHI), and Electronic Health Records (EHR). The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this Big Data and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. De-identified personal health care system is using Map Reduce, Pig Queries which are needed to be executed on the health care dataset. The application input dataset that contains the information of patients and de-identifies their personal health care. De-identification using Hadoop is also suitable for social and demographic data.