Page 58 - Big Data Analytics for Intelligent Healthcare Management

50 CHAPTER 3 BIG DATA ANALYTICS IN HEALTHCARE: A CRITICAL ANALYSIS

McGregor in [27] discusses the benefits and effectiveness of the use of big data in neonatal intensive
care units. He claims that it will lead to earlier discovery and deterrence of a wide range of fatal medical
conditions. The capability to process multiple high-speed physiological data streams from numerous
patients in numerous places and in real time could considerably improve both healthcare competence
and patient outcomes.
Fahim et al. [28] propose ATHENA (Activity-Awareness for Human-Engaged Wellness Applica-
tions) to plan and assimilate the association between the basic health needs and suggest the human
lifestyle and real-time recommendations for wellbeing services. With this system, their motive is to
develop a system to encourage an active lifestyle for individuals and to suggest valuable interferences
by making comparisons to their past habits. The proposed system processes sensory data through an
ML (machine learning) algorithm inside smart devices and exploits cloud infrastructure to decrease the
cost involved. Here, big data infrastructure is employed for huge sensory data storage and fast retrieval
for recommendations.
Das et al. [29] proposed a data-mining-based approach for the classification of diabetes mellitus
disease (DMD). They applied J48 and Naı ¨ve Bayesian techniques for the early detection of diabetes.
Their proposed model is elaborated in consecutive steps to help the medical practitioner to easily
explore and recognize the discovered rules better. The dataset used is collected from a college med-
ical hospital as well as from the online repository. Further practical applications are based on the
proposed approach. The PSO (particle swarm optimization) based approached can also be employed
for this classification task. One such type of classification technique can be found in [30]. This tech-
nique is a PSO-based evolutionary multilayer perceptron, which is trained using the back propagation
algorithm. Some other advanced techniques such as the one proposed in [31] can also be adopted for
the classification task. This technique [31] is based on the De-Bruijn graph with the MapReduce
framework, and it is used for metagenomic gene classifications. The graph-based MapReducing ap-
proach has two phases: mapping and reducing. In the mapping phase, a recursive naive algorithm is
employed to generate K-mers. The De-Bruijn graph is a compact representation of K-mers that finds
out an optimal path (solution) for genome assembly. The authors utilized similarity metrics for find-
ing similarity among the DNA (De-Oxy Ribonucleic Acid) sequences. In the reducing phase, Jaccard
similarity and purity of clustering are applied as dataset classifiers to classify the sequences based on
their similarity. The experimental results claim this technique is an efficient one for metagenomic
data clustering.
But it is also important to discuss the possible security threats that may arise while transferring med-
ical images and data over the internet. To deal with privacy and copyright protection of such a huge
amount of medical data, we need more robust and efficient techniques. These different techniques
should be studied and tested empirically for finding the most efficient technique that is easy to imple-
ment and can provide optimal protection. Such different techniques are thoroughly discussed in
[32, 33]. Different challenges that might be faced during the analysis of medical big data also need
to be addressed. A very good study on this can be found in [34].
In summary, above are some noteworthy contributions towards big data analytics in healthcare data.
These works contain some innovative ideas for utilizing big data analytics in healthcare data to extract
new valuable information and thereby discover innovative ways to deal with different serious diseases.
To deal with healthcare big data, there should be a sound architectural framework and then arises the
need for some big data analytics tools. These tools are briefly introduced, along with their advantages
and disadvantages in the below section.