International Journal of Modern Education and Computer Science (IJMECS)
ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)
Published By: MECS Press
IJMECS Vol.10, No.2, Feb. 2018
Big Data Analytics for Medical Applications
Full Text (PDF, 585KB), PP.35-42
Big Data is an accumulation of data sets which are abundant and intricate in character. They comprise both structured and unstructured data that evolve abundant, so speedy they are not convenient by classical relational database systems or current analytical tools. Big Data Analytics is not linearly able to expand. It is a predefined schema. Now big data is very helpful for backup of data not for everything else. There is always a data introducing. It also helps to solve India’s big problems. It also helps to fill the data gap. Health care is the conservation or advancement of health along the avoidance, interpretation and medical care of disorder, bad health, abuse, and other substantial and spiritual deterioration in mortal. Health care is expressed by health experts in united health experts, specialists, physician associates, mid-wife, nursing, antibiotic, pharmacy, psychology and other health. This paper focuses on providing information in the area of big data analytics and its application in medical domain. Further it includes introduction, Challenging aspects and concerns, Big Data Analytics in use, Technical Specification, Research application, Industry application and Future applications.
Cite This Paper
Nivedita Das, Leena Das, Siddharth Swarup Rautaray, Manjusha Pandey, " Big Data Analytics for Medical Applications", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.2, pp. 35-42, 2018.DOI: 10.5815/ijmecs.2018.02.04
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Shivam Goyal, Jaskirat Singh,"Two-Level Alloyed Branch Predictor based on Genetic Algorithm for Deep Pipelining Processors", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.5, pp.27-33, 2017.DOI: 10.5815/ijmecs.2017.05.04
Alexander, C., and Wang, L. Big data analytics in heart attack prediction. J Nurs Care 6, 393 (2017), 2167-1168.
Archenaa, J., and Anita, E. M. A survey of big data analytics in healthcare and government. Procedia Computer Science 50 (2015), 408-413.
Assuncao,M. D., Calheiros, R. N., Bianchi, S., Netto, M. A., and Buyya, R. Big data computing and clouds: Trends and future directions. Journal of Parallel and Distributed Computing 79 (2015), 3-15.
Chen, C. P., and Zhang, C.-Y. Data-intensive applications, challenges, tech-niques and technologies: A survey on big data. Information Sciences 275 (2014), 314-347.
Drey, N., Roderick, P., Mullee, M., and Rogerson, M. A population-based study of the incidence and outcomes of diagnosed chronic kidney disease. American Journal of Kidney Diseases 42, 4 (2003), 677-684.
Jokonya, O. Towards a big data framework for the prevention and control of hiv/aids, tb and silicosis in the mining industry. Procedia Technology 16 (2014), 1533-1541.
Kelly, J. A., Murphy, D. A., Sikkema, K. J., McAuliffe, T. L., Roffman, R. A., Solomon, L. J., Winett, R. A., Kalichman, S. C., and Collabo rative, T. C. H. P. R. Randomised, controlled, community-level hiv-prevention intervention for sexual-risk behaviour among homosexual men in us cities. The Lancet 350, 9090 (1997), 1500-1505.
Labrinidis, A., and Jagadish, H. V. Challenges and opportunities with big data. Proceedings of the VLDB Endowment 5, 12 (2012), 2032-2033.
Mohapatra, C., Das, L., Rautray, S. S., and Pandey, M. Map-reduce based modeling and dynamics of infectious disease. 895-898.
Murdoch, T. B., and Detsky, A. S. The inevitable application of big data to health care. Jama 309, 13 (2013), 1351-1352.
Patil, B. M., Joshi, R. C., and Toshniwal, D. E ective framework for pre-diction of disease outcome using medical datasets: clustering and classi cation. In-ternational Journal of Computational Intelligence Studies 1, 3 (2010), 273-290.
Prajapati, Vignesh. Big data analytics with R and Hadoop. Packt Publishing Ltd, 2013.
Raghupathi, W., and Raghupathi, V. Big data analytics in healthcare: promise and potential. Health information science and systems 2, 1 (2014), 3.
S. Packiyam, A. P. Big data analysis for aids disease detection system using clustering technique. International Journal of Computer Trends and Technology 48 (2017), 85-92.
Jeffrey, Annah M., Xiaohua Xia, and Ian K. Craig. "When to initiate HIV therapy: a control theoretic approach." IEEE transactions on Biomedical Engineering 50.11 (2003): 1213-1220.
Sadhana, S., and Shetty, S. Analysis of diabetic data set using hive and r. International Journal of Emerging Technology and Advanced Engineering 4, 7 (2014), 626-9.
Shamli, N., and Sathiyabhama, B. Parkinson's brain disease prediction using big data analytics. International Journal of Information Technology and Computer Science (IJITCS) 8, 6 (2016), 73.
Srivathsan, M., and Arjun, K. Y. Health monitoring system by prognotive computing using big data analytics. Procedia Computer Science 50 (2015), 602-609.
Zahid Ullah, Muhammad Fayaz, Asif Iqbal,"Critical Analysis of Data Mining Techniques on Medical Data", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.2, pp.42-48, 2016.DOI: 10.5815/ijmecs.2016.02.05
Tomar, D., and Agarwal, S. A survey on data mining approaches for healthcare. International Journal of Bio-Science and Bio-Technology 5, 5 (2013), 241-266.
Young, S. D. A big data approach to hiv epidemiology and prevention. Preventive medicine 70 (2015), 17-18.
Gardy, Jennifer L., et al. "Whole-genome sequencing and social-network analysis of a tuberculosis outbreak." New England Journal of Medicine 364.8 (2011): 730-739.
D'Agostino Sr, Ralph B., et al. "Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation." Jama 286.2 (2001): 180-187.
Palaniappan, Sellappan, and Rafiah Awang. "Intelligent heart disease prediction system using data mining techniques." Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on. IEEE, 2008.
Fouque, D., et al. "A proposed nomenclature and diagnostic criteria for protein–energy wasting in acute and chronic kidney disease." Kidney international 73.4 (2008): 391-398.
John Walker, Saint. "Big data: A revolution that will transform how we live, work, and think." (2014): 181-183.
Davenport, Thomas H., Paul Barth, and Randy Bean. "How big data is different." MIT Sloan Management Review 54.1 (2012): 43.
Lazer, David, et al. "The parable of Google Flu: traps in big data analysis." Science 343.6176 (2014): 1203-1205.
McAfee, Andrew, Erik Brynjolfsson, and Thomas H. Davenport. "Big data: the management revolution." Harvard business review90.10 (2012): 60-68.
O’Driscoll, Aisling, Jurate Daugelaite, and Roy D. Sleator. "‘Big data’, Hadoop and cloud computing in genomics." Journal of biomedical informatics 46.5 (2013): 774-781.
Patel, Aditya B., Manashvi Birla, and Ushma Nair. "Addressing big data problem using Hadoop and Map Reduce." Engineering (NUiCONE), 2012 Nirma University International Conference on. IEEE, 2012.
S. Packiyam,et al. “Aids Detection System Using Big Data Analysis.” IJANA (International Journal of Advanced Networking & Applications), (2017):105-109.