BIG DATA ANALYTICS FOR HEALTHCARE INDUSTRY: IMPACT, APPLICATIONS, AND TOOLS

In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by
various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The
health industry sector has been confronted by the need to manage the big data being produced by various
sources, which are well known for producing high volumes of heterogeneous data. Various big-data analytics
tools and techniques have been developed for handling these massive amounts of data, in the healthcare
sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the
Hadoop ecosystem for handling it. Hadoop is an Apache open source framework written in java that allows
distributed processing of large datasets across clusters of computers using simple programming models. The
Hadoop framework application works in an environment that provides distributed storage and computation
across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines,
each offering local computation and storage. In this paper we also explore the conceptual architecture of big
data analytics for healthcare which involves the data gathering history of different branches, the genome
database, electronic health records, text/imagery, and clinical decisions support system.