BIG DATA ANALYSIS IN HEALTH CARE DOMAIN: A SYSTEMATIC REVIEW
As the Volume of the data produced is increasing day by day in our society, the exploration of big data in healthcare is increasing at an unprecedented rate. Now days, Big data is very popular buzzword concept in the various areas. This paper provide an effort is made to established that even the healthcare industries are stepping into big data pool to take all advantages from its various advanced tools and technologies. This paper provides the review of various research disciplines made in health care realm using big data approaches and methodologies. Big data methodologies can be used for the healthcare data analytics (which consist 4 V’s) which provide the better decision to accelerate the business profit and customer affection, acquire a better understanding of market behaviours and trends and to provide E-Health services using Digital imaging and communication in Medicine (DICOM).Big data Techniques like Map Reduce, Machine learning can be applied to develop system for early diagnosis of disease, i.e. analysis of the chronic disease like- heart disease, diabetes and stroke. The analysis on the data is performed using big data analytics framework Hadoop. Hadoop framework is used to process large data sets Further the paper present the various Big data tools , challenges and opportunities and various hurdles followed by the conclusion.
David Becker, Bill mcmullen, Trish Dunn King, “Big Data, Big Data Quality Problem”, IEEE International Conference on the Big data (Big data ), 2015 DOI: https://doi.org/10.1109/BigData.2015.7364064
Aslam M.A and Abdullah.A “A Methodology and a tool to prepare Agro-meterological maps as source of Big Data” Multimedia Big Data (Big MM) ,2015 IEEE International Conference, Beijing, 2015, pp. 208-211 DOI: https://doi.org/10.1109/BigMM.2015.42
Khan, M.A., Uddin, M.F., Gupta, N.: Seven V’s of big data. In: ASEE Zone 1, Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) (2014).
Xindong Wu, Fellow, Xingquan Zhu,Gong-Qing Wu, and Wei Ding (2014) “Data Mining with Big Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No 1, pp.97-107. DOI: https://doi.org/10.1109/TKDE.2013.109
EMC Digital Universe & IDC: The digital universe: driving data growth in healthcare, challenges and opportunities (2004).
Suthaharan, Shan. "Big data classification: Problems and challenges in network intrusion prediction with machine learning." ACM SIGMETRICS Performance Evaluation Review 41.4 (2014): 70-73. DOI: https://doi.org/10.1145/2627534.2627557
Prof Jigna Ashish Patel, Dr. Priyanka Sharma “Big data for better health planning”,IEEE International conference on advances in Engineering & Technology research, August 2014
B.Blobel , D M Lopez & C Gonzalez, “Patient privacy and security concerns on Big Data for personalized medicine”, Springer – Verlag Berlin Heidelberg
Kiyana Zolfaghar, Naren Meadem, Ankur Teredesai, Senjuti Basu Roy, Si-Chi Chin and Brian Muckian (2013) “Big Data Solutions for Predicting Risk-of-Readmission for Congestive Heart Failure”, IEEE International Conference on Big Data Vol.3, No 6, pp. 64-71. DOI: https://doi.org/10.1109/BigData.2013.6691760
What is IBM Watson http://www.ibm.com/smarterplanet/us/en/ibmwatson/whatiswatsonhtml.
Large-scale machine learning for drug discovery. http://googleresearch.blogspot.in/2015/03/ large-scale-machine-learning-for-drug.html
Ferlie, E.B., Shortell, and S.M.: Improving the quality of healthcare in the United Kingdom and the United States: a framework for change. 79, 281–315 (2001)
Sun, J., Reddy, C.K.: Big data analytics for healthcare. In: SIAM International Conference on Data Mining (2013) DOI: https://doi.org/10.1145/2487575.2506178
Mohd Rehan Ghazia, Durgaprasad Gangodkara: Hadoop, MapReduce and HDFS: A Developers Perspective: International Conference on Intelligent Computing, Communication & Convergence (ICCC-2014).
G.Vaishali, V.Kalaivani : BIG DATA ANALYSIS FOR HEART DISEASE DETECTION SYSTEM USING MAP REDUCE TECHNIQUE.
Saravana N, M Ramachandran and S Lavanya Kumar (2015),” Predictive Methodology for Diabetic Data Analysis in Big Data”, ScienceDirect - Procedia Computer Science Vol 50, pp. 203 – 208. DOI: https://doi.org/10.1016/j.procs.2015.04.069
Rama Satish, K. V., and N. P. Kavya. "Big data processing with harnessing hadoop-MapReduce for optimizing analytical workloads." Contemporary Computing and Informatics (IC3I), 2014 International Conference on. IEEE, 2014. DOI: https://doi.org/10.1109/IC3I.2014.7019818
Victor L. Voydock and Stephen T. Kent (1983): Security mechanisms in high-level network protocols. ACM Comput. Surv 15(2):135–171, DOI: https://doi.org/10.1145/356909.356913
Puneet Singh Duggal, Sanchita Paul, ― Big Data Analysis: Challenges and Solutions‖, International Conference on Cloud, Big Data and Trust 2013, Nov 13-15, RGPV.
Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare: promise and potential." Health Information Science and Systems 2.1 (2014): 3 DOI: https://doi.org/10.1186/2047-2501-2-3
Copyright (c) 2018 Abhishek Bajpai, Dr. Sanjiv Sharma
This work is licensed under a Creative Commons Attribution 4.0 International License.
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section