International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3674 AUTOMATED HEALTH CARE MANAGEMENT SYSTEM USING BIG DATA TECHNOLOGY Ms.A.Sivasankari.,Mrs.N.Sindhuja.,Mrs.S.Selvakani., 1 Head of the department, Department of Computer science, DKM College for Women (Autonomous), Vellore. 2 Research scholar,Department of Computer Science, DKM College for Women (Autonomous), Vellore, TamilNadu. 3 Department of Computer Science,Thiruvalluvar university college arts and science,Arakkonam,Tamilnadu. ---------------------------------------------------------------------***--------------------------------------------------------------------- ABSTRACT – Generally Automation plays an important role in the global economy and in daily experience. The Automated Healthcare Management System is an automated system that is used to manage patient information and its administration. In existing system challenges at large scale performing large-scale computation is difficult. To work with this volume of data requires distributing parts of the problem to multiple machines to handle in parallel. The information gained from analyzing massive amounts of aggregated health data can provide useful insight to improve quality and efficiency for providers and insurers a like.This makes the patients reach out for healthcare solutions easily and cheaply and makes healthcare a easy reach for the unprivileged also. Thus, this unified model can serve as a data collection, delivery as well as an analytic tool in the healthcare domain.This paper addresses the problem of data quality in electronic patient records using a computerized patient records report system with Apache HIVE and abstraction of Map reduce of big data technology. We analyzed which patient is spending more money than the others with the Map reduce. We got the data to be processed from traditional system to Hadoop via ETL's. We organized this with Oozie scheduler in Hadoop. The data what you are going to analyze is an semi-structured data. After uploading their data to cluster anyone can access them again provided they got to be in the cluster or can also use virtual machines that contain the right software to analyze them without any need for conversion. Key words – AHMS, Oozie, HIVE, Mapreduce,ETLs,Hadoop. 1.INTRODUCTIONS Generally Automation plays an important role in the global economy and in daily experience. Engineers trying to combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications. The Automated Healthcare Management System is an automated system that is helps to manage patient information and its administration. It is meant to give the Administration and Staff, with information in practical to make their work more interesting and less stressing. Whenever multiple machines are used in cooperation with one another, the probability of failures rises. In a single- machine environment, failure is not something that program designers explicitly worry about very often: if the machine has crashed, then there is no way for the program to recover anyway. This is because of the fact that a set of particular symptoms will not always lead to a particular disease and can be causing another set of diseases. So we may be able to tap the appropriate set of diseases linked to the symptoms easily after analysis. This model also meets the key challenges posed to us in health sector that are shortage of human resources in the sector, accessibility of healthcare infrastructure, affordability of healthcare services especially for the rural population. Some major benefits from the model includes: •Cutting down recurring medical costs •Well- maintained medical history Secured medical records accessible any-time anywhere •Centralised system with patients having personalised dashboard for self monitoring as well as for surveillance by the doctors. •Socio-demographic factors and locations of patients taped and analysed Thus, this research model can be a great tool in data collection as well as produce real-time data analytics insights 2.EXISTING SYSTEM In existing system facing problems at large scale performing large-scale computation is difficult. To work with this amount of data requires distributing parts of the problem to multiple machines to handle in parallel. Whenever various machines are used in cooperation with one another, the chance of failures rises [2]. In a single-machine environment,