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Big Data Analytics in Health Care

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Emerging Research in Data Engineering Systems and Computer Communications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1054))

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Abstract

In today’s world, data is growing exponentially and widespread accessibility of data led to analyze and visualize data effectively using analytical techniques in healthcare industry. Big data analytics play a vital role and provides long-term benefits in tremendously handling huge explosive data. In this paper, we present an overview of different big data platform tools and different technologies that support big data analytics in health care. It also describes different steps involved in big data analytics process and also presents ways to improve health care by considering various facts by using big data analytics. As big data analytics has the potential to provide useful insight in health care, this article uses a review methodology to categorize the uses of big data in health care. This study provides a baseline to assess the essential prospects of computational health informatics and the use of big data in health care in understanding different scopes of big data platforms.

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References

  1. Improved Approaches to Handle Bigdata through Hadoop KLEF University, India

    Google Scholar 

  2. Y. Demchenko, Z. Zhao, P. Grosso, A. Wibisono, C. de Laat: Addressing big data challenges for scientific data infrastructure. In: IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom 2012). IEEE Computing Society, based in California, USA, Taipei, Taiwan, pp. 614–617 (2012)

    Google Scholar 

  3. Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K., Taha, K.: Efficient machine learning for big data: a review. Data Res. 2, 87–93 (2015). https://doi.org/10.1016/j.bdr.2015.04.001

    Article  Google Scholar 

  4. Chen, H., Fuller, S.S., Friedman, C., Hersh, W.: Medical Informatics: Knowledge Management and Data Mining in Biomedicine, 8. Springer Science & Business Media (2006)

    Google Scholar 

  5. Ritu, C., Jangade, R.: A robust model for big healthcare data analytics. Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference. IEEE, New York (2016)

    Google Scholar 

  6. Sadilek, A., Kautz, H., Silenzio, V.: Modeling spread of disease from social interactions. In: Sixth AAAI International Conference on Weblogs and Social Media (ICWSM) (2012). http://www.cs.rochester.edu/~kautz/papers/Sadilek-KautzSilenzio_Modeling-Spread-of-Disease-from-SocialInteractions_ICWSM-12.pdf

  7. Erl, T., Khattak, W., Buhler, P.: Big Data Fundamentals: Concepts, Drivers & Techniques. Prentice Hall. Part of the The Prentice Hall Service Technology Series from Thomas Erl Series (2016 Jan 5)

    Google Scholar 

  8. Agrawal, D., et. al.: Challenges and Opportunities with Big Data. Big Data White Paper-Computing Research Association (2012 Feb). Available http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf

  9. Hashem, I.A.T., Yaqoob, I., Badrul Anuar, N., Mokhtar, S., Gani, A., Ullah Khan, S.: The rise of “Big Data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2014). https://doi.org/10.1016/j.is.2014.07.006

  10. Archenaa, J., Mary Anita, E.A.: A survey of big data analytics in healthcare and government. Procedia Comput. Sci. 50, 408–413 (2015). Big Data, Cloud and Computing Challenges. Available: http://www.sciencedirect.com/science/article/pii/S1877050915005220

  11. Wicks, P., Massagli, M., Frost, J., Brownstein, C., Okun, S., Vaughan, T., et al.: Sharing health data for better outcomes on PatientsLikeMe. J. Med. Internet Res. 12, e19 (2010). https://doi.org/10.2196/jmir.1549

    Article  Google Scholar 

  12. U.S. Government, Department of Health and Human Services, Federal Register, Rules and Regulations, 74(2009)56123-56131. Available from: https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/administrative/enforcementrule/enfir.pdf

  13. Sai Jyothi, B., Jyothi, S.: Doc-based modelling for medical big data. IADS SSRN: https://ssrn.com/abstract=3170156, Volume No. 01, Issue No. 03.[Scopus] (2018)

  14. Russom, P.: Big data analytics; TDWI best practices report; Fourth Quarter; Report No.: 9.14.2011; TDWI: Renton, WV, USA (2011)

    Google Scholar 

  15. Mohammed, E.A., Far, B.H., Naugler, C.: Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends. BioData Min. 7, 22 (2014)

    Article  Google Scholar 

  16. The R project for statistical computing. http://www.r-project.org/

  17. Patel, J.A., Sharma, P.: Big data for better health planning. In: IEEE International Conference on Advances in Engineering & Technology Research, August 2014

    Google Scholar 

  18. https://hive.apache.org/

  19. Hows, D., Membrey, P., Plugge, E., Hawkins, T.: Introduction to mongodb. In: The Definitive Guide to MongoDB, 16, p. 1. Springer, Berkeley, CA (2015)

    Google Scholar 

  20. http://en.wikipedia.org/wiki/Apache_Cassandra

  21. Gowsalya, M., Krushitha, K., Valliyammai, C.: Predicting the risk of readmission of diabetic patients using MapRe-duce. 2014 Sixth International Conference on Advanced Computing (ICoAC). IEEE, New York (2014)

    Google Scholar 

  22. Gomathi, S., Narayani, V.: Implementing big data analytics to predict systemic lupus erythematosus. In: 2015 International Conference on Innovations in In-formation, Embedded and Communication Systems (ICIIECS). IEEE, New York (2015)

    Google Scholar 

  23. Sheriff, C.I., Naqishbandi, T., Geetha, A.: Healthcare informatics and analytics framework. 2015 International Conference on Computer Communication and Informatics (ICCCI). IEEE, New York (2015)

    Google Scholar 

  24. Prasad, S.T., et al.: Diabetic data analysis in big data with predictive method. 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, New York (2017)

    Google Scholar 

  25. Ojha, M., Mathur, K.: Proposed application of big data analytics in healthcare at Maharaja Yeshwantrao Hospital. 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, New York (2016)

    Google Scholar 

  26. Kalyankar, G.D., Poojara, S.R., Dharwadkar, N.V.: Predictive analysis of diabetic patient data using machine learning and Hadoop. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, New York (2017)

    Google Scholar 

  27. Chennamsetty, H., Chalasani, S., Riley, D.: Predictive analytics on Electronic Health Records (EHRs) using Hadoop and Hive. 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, New York (2015)

    Google Scholar 

  28. Asri, H., et al.: Big data in healthcare: challenges and opportunities. 2015 International Conference on Cloud Technologies and Applications (CloudTech). IEEE, New York (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Wadi Addawasir, Riyadh, Saudi Arabia

    Tahmeena Fatima

  2. Department of Computer Science, SPMVV, Tirupati, India

    Singaraju Jyothi

Authors
  1. Tahmeena Fatima
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  2. Singaraju Jyothi
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Corresponding author

Correspondence to Tahmeena Fatima .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Sri Padmavati Mahila University, Tirupathi, Andhra Pradesh, India

    P. Venkata Krishna

  2. Department of ECE, Nazarbayev University, Astana, Kazakhstan

    Mohammad S. Obaidat

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Fatima, T., Jyothi, S. (2020). Big Data Analytics in Health Care. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_36

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