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

Authors

  • Subhadra K Research scholar Nehru arts and science Coimbatore, India
  • Kavitha N Head of the department Computer science Nehru arts and science college Coimbatore, India

DOI:

https://doi.org/10.26438/ijcse/v6si8.1517

Keywords:

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

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

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.

Downloads

Published

2025-11-17
CITATION
DOI: 10.26438/ijcse/v6si8.1517
Published: 2025-11-17

How to Cite

[1]
K. Subhadra and N. Kavitha, “Big Data Analytics for Health Care Applications Using Cloud Computing- A Study”, Int. J. Comp. Sci. Eng., vol. 6, no. 8, pp. 15–17, Nov. 2025.

Issue

Vol. 6 No. 8 (2018): IJCSE Special Issue October Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.