Abstract
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. 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.
Highlights
Every day, data is generated by a range of different applications, devices, and geographical research activities for the purposes of weather forecasting, weather prediction, disaster evaluation, crime detection, and the heath industry, to name a few
The various terminologies and models that have been developed to resolve the problems associated with big data focus on solving four issues known as the four Vs, namely: volume, variety, velocity, and veracity
We have provided an in-depth description and a brief overview of big data in general and in healthcare system, which plays a significant role in healthcare informatics and greatly influences the healthcare system and the big data four Vs in healthcare
Summary
Data is generated by a range of different applications, devices, and geographical research activities for the purposes of weather forecasting, weather prediction, disaster evaluation, crime detection, and the heath industry, to name a few. As. Sunil Kumar et al.: Big Data Analytics for Healthcare Industry: Impact, Applications, and Tools variety as a result of the linking of a diverse range of biomedical data sources including, for example, sensor data, imagery, gene arrays, laboratory tests, free text, and demographics[5]. Most data in healthcare system (e.g., doctor’s notes, lab test results, and clinical data) is unstructured and is not stored electronically, i.e., it exists only in hard copies and its volume is increasing very rapidly. The various classes of data in healthcare applications include Electronic Health Records (EHR), machine generated/sensor data, health information exchanges, patient registries, portals, genetic databases, and public records. Public records are major sources of big-data in the healthcare industry and require efficient data analytics to resolve their associated healthcare problems. The identification of health features in medical data and the selection of class attributes for health analytics demands highly sophisticated and architecturalyl specific techniques and tools
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