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Big Data Analytics for Health Care Applications Using Cloud Computing- A Study

Subhadra K.1 , N Kavitha2

Section:Research Paper, Product Type: Journal Paper
Volume-06 , Issue-08 , Page no. 15-17, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6si8.1517

Online published on Oct 31, 2018

Copyright © Subhadra K., N Kavitha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Subhadra K., N Kavitha, “Big Data Analytics for Health Care Applications Using Cloud Computing- A Study,” International Journal of Computer Sciences and Engineering, Vol.06, Issue.08, pp.15-17, 2018.

MLA Style Citation: Subhadra K., N Kavitha "Big Data Analytics for Health Care Applications Using Cloud Computing- A Study." International Journal of Computer Sciences and Engineering 06.08 (2018): 15-17.

APA Style Citation: Subhadra K., N Kavitha, (2018). Big Data Analytics for Health Care Applications Using Cloud Computing- A Study. International Journal of Computer Sciences and Engineering, 06(08), 15-17.

BibTex Style Citation:
@article{K._2018,
author = {Subhadra K., N Kavitha},
title = {Big Data Analytics for Health Care Applications Using Cloud Computing- A Study},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2018},
volume = {06},
Issue = {08},
month = {10},
year = {2018},
issn = {2347-2693},
pages = {15-17},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=467},
doi = {https://doi.org/10.26438/ijcse/v6i8.1517}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i8.1517}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=467
TI - Big Data Analytics for Health Care Applications Using Cloud Computing- A Study
T2 - International Journal of Computer Sciences and Engineering
AU - Subhadra K., N Kavitha
PY - 2018
DA - 2018/10/31
PB - IJCSE, Indore, INDIA
SP - 15-17
IS - 08
VL - 06
SN - 2347-2693
ER -

           

Abstract

This paper focuses the art of big data in medical field general background of big data is discussed first and then its related areas, such as cloud computing, data centers, internet of things and Hadoop. The value chain four phases are discussed here that is data generation, data acquisition, data storage and data analysis. For each phase here we are introducing general background technical challenges and review the latest advantages. Big data is a concept which defines the difference between itself and “massive data” or “very big data”. Three v s of big data are volume, velocity and variety which are defined by Doung Laney in 2001.

Key-Words / Index Term

Big data analytics, information management, literature review, health care, data driven application

References

[1]. W. Raghupathi, V. Raghupathi, "Big data analytics in healthcare: promise and potential", Health Information Science and Systems, vol. 2, pp. 1-10, 2014. WHO. Mobile phones help people with diabetes to manage fasting and feasting during Ramadan. Features. 2014.
[2]. David Houlding, MSc, CISSP. Health Information at Risk: Successful Strategies for Healthcare Security and Privacy. Healthcare IT Program Of ce Intel Corporation, white paper. 2011.
[3]. Giangregorio LM, Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, et al. FRAX underestimates fracture risk in patients with diabetes. J Bone Miner Res. 2012;27(2):301–8.
[4]. Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2007;2:59–77.
[5]. Witten IH (Ian H., Frank E, Hall MA (Mark A, Pal CJ. Data mining: practical machine learning tools and techniques. 621 p.
[6]. Maglogiannis IG. Emerging artificial intelligence applications in computer engineering: real word AI systems with applications in eHealth, HCI, information retrieval and pervasive technologies. IOS Press; 2007. 407 p.
[7]. Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2005;163(3):262–70.
[8]. Nemes S, Jonasson JM, Genell A, Steineck G. Bias in odds ratios by logistic regression modelling and sample size. BMC Med Res Methodol. 2009;9(1):56.
[9]. Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health: integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol 2005;161(1):81–88.
[10]. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–8.