A Collective Study of Machine Learning (ML) Algorithms with Big Data Analytics (BDA) for Healthcare Analytics (HcA)
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Pradeep K R, Dr.Naveen N C|
|DOI : 10.14445/22312803/IJCTT-V47P121|
Pradeep K R, Dr.Naveen N C "A Collective Study of Machine Learning (ML) Algorithms with Big Data Analytics (BDA) for Healthcare Analytics (HcA)". International Journal of Computer Trends and Technology (IJCTT) V47(3):149-155, May 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Big Data Analytics (BDA) is one of the rising innovations as it guarantees to give better gain of knowledge from gigantic and heterogeneous information. BDA includes choosing the appropriate Big Data (BD) storage and calculated structure expanded by adaptable Machine Learning (ML) techniques. In spite of the huge buzz around BDA and its focal points to new problems, novel approaches of data capture, storage, analysis and visualization are accountable in favor of the materialization of the BDA research field. ML algorithms can be used in BDA to construct finer and extra precise inferences. Though the challenges BD imposes, these algorithms necessitate by adapting and optimizing to precise applications. This research work focuses and describes different ML algorithms in BDA that are useful in Health care Analytics (HcA).
 T.M. Mitchell, Machine Learning, McGraw Hill, New York, 1997.
 X.W. Chen, and X Lin, Big data deep learning: challenges and perspectives, IEEE Access 2 (2014) 514-525.
 N Jones, Computer science: the learning machines, Nature 505 (2014) 146-148
 Singh, Jainendra. "Real time BIG data analytic: Security concern and challenges with Machine Learning algorithm", 2014 ,Conference on IT in Business Industry and Government (CSIBIG), 2014
 J. F¨urnkranz et al., Foundations of Rule Learning, Cognitive Technologies, DOI 10.1007/978-3-540-75197-7 1, © Springer-Verlag Berlin Heidelberg 2012
 Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p.89.ISBN 978-1-118- 63817-0.
 Big data analytics in healthcare: promise and potential Wullianallur Raghupathi1 and Viju Raghupathi Health Information Science and Systems 2014, 2:3 http://www.hissjournal.com/content/2/1/3
 Chen, R., Sivakumar, K., & Kargupta, H. (2004). Collective mining of Bayesian networks from distributed heterogeneous data. Knowledge and Information Systems, 6, 164–187
 Ben-Gal, I., Dana, A., Shkolnik, N., & Singer, G. (2014). Efficient construction of decision trees by the dual information distance method. Quality Technology & Quantitative Management (QTQM), 11, 133–147.
 Kogge, P. M., & Bayliss, D. (2013). Comparative performance analysis of a Big Data NORA problem on a variety of Architectures. In Geoffrey, F., Waleed, W. S. (Eds.), In Collaboration technologies and systems (CTS), 2013 International Conference on (pp. 22–34). Washington, DC, USA: IEEE. Soysal, Ö. M. (2015). Association rule mining with mostly associated sequential patterns. Expert Systems with Applications, 42, 2582–2592.
 Nakanishi, T ,”A data-driven axes creation model for correlation measurement on big data analytics” Information Modelling and Knowledge Bases XXVI, 272, 308, 2014
 Qin, C., & Rusu, F. (2013). Scalable I/O-bound parallel incremental gradient descent for big data analytics in Glade. In Proceedings of the second workshop on data analytics in the cloud (pp. 16–20). New York, NY, USA: ACM.
 Cheptsov, A., & Koller, B. (2013). A service-oriented approach to facilitate big data analytics on theWeb. In Bathe, K.J., Topping, B.H.V. (Eds.), In Proceedings of the fourteenth international conference on civil, structural and environmental engineering computing. Stirlingshire, UK: Civil-Comp Press.
 https://tdwi.org/research/2016/07/best-practices-reportimproving- data-preparation-for-business-analytics.aspx
 Blackett, G. (2013). Analytics network – O.R. & analytics [online].Retrievedfromhttps://www.theorsociety.com/Pages/ SpecialInterest/AnalyticsNetwork_analytics.aspx
 Shekhar, Himanshu, and Manoj Sharma. "A Framework for Big Data Analytics as a Scalable Systems."Special Conference Issue: National Conference on Cloud Computing & Big Data 72- 82,IJANA,http://www.ijana.in/Special%20Issue/C14.pdf.
 S. K. Pal, V. Talwar, and P. Mitra, „„Web mining in soft computing framework: Relevance, state of the art and future directions,’’ IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1163–1177, 2002.
 D. J. Watts, Six Degrees: The Science of a Connected Age. New York, NY, USA: Norton, 2004.
 J. E. Hirsch, “An index to quantify an individual’s scientific research output,’’ Proc. Nat. Acad. Sci. United States Amer., vol. 102, no. 46, p. 16569, 2005.
 [55-57] H. Zhang, Z. Zhang, and H. Dai, „„Gossip-based information spreading in mobile networks,’’ IEEE Trans. Wireless Commun., vol. 12, no. 11, pp. 5918– 5928, Nov. 2013.
 H. Zhang, Z. Zhang, and H. Dai, „„Mobile conductance and gossip-based information spreading in mobile networks,’’ in IEEE Int. Symp. Inf. Theory Proc. (ISIT), Jul. 2013, pp. 824–828
 H. Zhang, Y. Huang, Z. Zhang, and H. Dai. (2014). Mobile conductance in sparse networks and mobilityconnectivity tradeoff. in Proc. IEEE Int. Symp. Inf. Theory (ISIT) [Online]. Available: http://www4. ncsu.edu/?hdai/ISIT2014-HZ.pdf
 Burghard C. Big Data and Analytics Key to Accountable Care Success. 2012.
 Sandhya N. dhage, Charanjeet Kaur Raina “A review on Machine Learning Techniques” In: Proceedings of International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 4, Issue: 3 (pp. 395– 399) ISSN: 2321-8169
 http://www.skytree.net/machine-learning/why-do-machinelearning- big-data/
 David Edward Marcinko, Hope Rachel Hertico, “Financial Management Strategies for Hospitals and Healthcare Organizations: Tools, Techniques, Checklists and Case Studies”, 5 September 2013, CRC Press.
 https://www.hpe.com/h20195/v2/GetPDF.aspx/4AA6- 7132ENW.pdf
 IHTT, Transforming Health Care through Big Data Strategies for leveraging big data in the health care industry.2013
 Chen, C. L. P. & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275(10), 314-347.
 Suthaharan, S. (2014). Big data classification: problems and challenges in network intrusion prediction with machine learning. Performance Evaluation Review, 41(4), 70-73.
 Prateek Bihani1 and S. T. Patil “A Comparative Study of Data Analysis Techniques “ ,ijettcs Volume 3, Issue 2, March – April 2014
 LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N. Big data, analytics and the path from insights to value. MIT Sloan Manag Rev. 2011; 52:20–32.
Machine Learning, Big Data, Big Data nalytics, , Healthcare Analytics.