Title: Diabetic data analysis in healthcare using Hadoop architecture over big data

Authors: P. Sampath; S. Tamilselvi; N.M. Saravana Kumar; S. Lavanya; T. Eswari

Addresses: Department of CSE, Sasurie College of Engineering, Vijayamangalam, Perundurai, Tamil Nadu, India ' Department of Biotechnology, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India ' Department of IT, Vivekanandha College of Engineering for Women, Tiruchengode, Tamil Nadu, India ' Department of IT, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India ' Department of IT, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Abstract: Owing to the increasing developments in this digitised era, it is necessary to move from paper health records to digital by handling the large volume of healthcare data for analysis, and using them for efficient treatment will be a crucial issue. Diabetes Mellitus (DM) is one of the Non-Communicable Diseases (NCD). It is a major health hazard in developing countries and associated with long-term complications and numerous health disorders. The main idea of this project is to integrate massive unstructured diabetic data from various sources which need to be normalised into a proper scale to get optimised solution for medicinal field using Hadoop. Although the traditional database management systems can handle data effectively, processing the high volume of unstructured data at a reasonable time becomes very challenging. This paper uses the predictive analysis algorithm in the Hadoop/MapReduce environment to predict the DM complexities and the type of treatment to be adopted. Based on the analysis, this system provides an efficient way to cure patients with better outcomes.

Keywords: predictive analysis; Hadoop MapReduce; healthcare technology; diabetes mellitus; big data analytics; diabetic data analysis; diabetes treatment.

DOI: 10.1504/IJBET.2017.082655

International Journal of Biomedical Engineering and Technology, 2017 Vol.23 No.2/3/4, pp.137 - 147

Received: 11 Jun 2016
Accepted: 26 Aug 2016

Published online: 24 Feb 2017 *

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