Page 8 - Big Data Analytics for Intelligent Healthcare Management

Preface

Nowadays, the biggest technological challenge in big data is to provide a mechanism for storage,
manipulation, and retrieval of information on large amounts of data. In this context, the healthcare in-
dustry is also being challenged with difficulties in capturing data, storing data, analyzing data, and data
visualization. Due to the rapid growth of the large volume of information generated on a daily basis, the
use of existing infrastructure has become impracticable to handle this issue. So, it is essential to develop
better intelligent techniques, skills, and tools to automatically deal with patient data and its inherent
insights. Intelligent healthcare management technologies can play an effective role in tackling this
challenge and change the future for improving our lives. Therefore, there are increasing interests in
exploring and unlocking the value of the massively available data within the healthcare domain.
Healthcare organizations also need to continuously discover useful and actionable knowledge and gain
insight from raw data for various purposes such as saving lives, reducing medical errors, increasing
efficiency, reducing costs, and improving patient outcome. Thus, data analytics in intelligent health-
care management brings a great challenge and also plays an important role in intelligent healthcare
management systems.
In the last decade, huge advances in the large scale of data due to the smart devices has led to the
development of various intelligent technologies. These smart devices continuously produce very large
amounts of structured and unstructured data in healthcare, which is difficult to manage in real life
scenarios. Big data analytics generally use statistical and machine learning techniques to analyze huge
amounts of data. These high dimensional data with multiobjective problems in healthcare is an open
issue in big data. Healthcare data is rapidly growing in volume and multidimensional data. Heteroge-
neous healthcare data in various forms such as text, images, video, etc., are required to be effectively
stored, processed, and analyzed to avoid the increasing cost of healthcare and medical errors. This rapid
expansion of data leads to urgent development of intelligent healthcare management systems for
analysis.
The main objective of this edited book is to cover both the theory and applications of hardware
platforms and architectures, development of software methods, techniques and tools, applications
and governance, and adoption strategies for the use of big data in healthcare and clinical research.
It aims to provide an intellectual forum for researchers in academia, scientists, and engineers from
a wide range of applications to present their latest research findings in this area and to identify future
challenges in this fledging research area.
To achieve the objectives, this book includes eleven chapters, contributed to by promising authors.
In Chapter 1, Gill et al. highlighted a broad methodical literature analysis of bio-inspired algorithms
for big data analytics. This chapter will also help in choosing the most appropriate bio-inspired
algorithm for big data analytics in a specific type of data along with promising directions for future
research. In Chapter 2, the author’s objective is to examine the potential impact of immense data chal-
lenges, open research issues, and distinctive instrument identification in big data analytics. In
Chapter 3, the author includes every possible terminology related to the idea of big data, healthcare
data, and the architectural context for big data analytics, different tools, and platforms are discussed
in details.

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