Big Data Analysis for Aids Disease Detection System using Clustering Technique

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-48 Number-2
Year of Publication : 2017
Authors : S. Packiyam, A. Prema
DOI :  10.14445/22312803/IJCTT-V48P118

MLA

S. Packiyam, A. Prema "Big Data Analysis for Aids Disease Detection System using Clustering Technique". International Journal of Computer Trends and Technology (IJCTT) V48(2):85-92, June 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Big data analysis is the demanding one because it contains large amount of records. In today’s world, the massive information in health care is to be processed in order to recognize, diagnose, detect and prevent the various diseases. It is projected to develop a centralized patient monitoring system using big data. In the planned system, large set of medical records are full as input. From this medical data set, it is aimed to extract the required information from the record of AIDS patients using clustering technique. The classification process states whether the patient is normal or abnormal and in the detection step using clustering technique to detect the disease and decrease the dataset. Thus, the proposed system helps to classify a large and complex medical dataset and detect the AIDS disease. Hadoop is the most popular platform for big data analysis. The Hadoop ecosystem is vast and involves many supporting frameworks and tools to effectively run and manage it. This article focuses on the center of Hadoop concepts and its technique to handle data.

References
[1] Bhawna Gupta and Dr. Kiran Jyoti (2014), “Big data Analytics with Hadoop to analyze Targeted Attacks on Enterprise Data”, (IJCSIT) International Journal of Computer Science and Information Technologies.
[2] Devi.L and S.Gowri (2015), “Optimizing Cluster functionality in bigdata using Cache Manager”, ARPN Journal of Engineering and Applied Sciences.
[3] Kiyana Zolfaghar, Naren Meadem, Ankur Teredesai, Senjuti Basu Roy, Si-Chi Chin and Brain Muckian(2013) “Big Data Solutions for Predicting Risk-of-Readmission for Congestive AIDS”, IEEE International Conference on Big Data.
[4] Muni Kumar N and Manjula R (2014), “Role of Big Data Analytics in Rural Health Care – A Step Towards Svasth Bharath”, (IJCSIT) International Journal of Computer Science and Information Technologies.
[5] Pradeepa A, Dr. Antony Selvadoss Thanamani (2013), “ Hadoop File System and Fundamental Concept of Cluster Interior and Closure Rough Set Approximations”, International Journal of Advanced Research in Computer and Communication Engineering.
[6] Praveen Kumar and Dr. Vijay Singh Rathore (2014), “ Efficient Capabilities of Processing of Big Data using Hadoop Cluster”, International Journal of Advanced Research in Computer and Communication Engineering.
[7] Saravana N, M Ramachandran and S. Lavanya Kumar (2015) , “ Predictive Metodology for Diabetic Data Analysis in Big Data”, ScienceDirect-Procedia Computer Science.
[8] Sathiyavathi R (2015), “ A Survey: Big Data Analytics on Healthcare System”, HIKARI Ltd Contemporary Engineering Sciences.
[9] K. Sharmila and Dr. S.A.Vethamanickam (2015), “Survey on Data Mining Algorithm and Its Application in Healthcare Sector Using Hadoop Platform”, International Journal.
[10] Xindong Wu, Fellow, Xingquan Zhu, Gong-Qing Wu and Wei Ding (2014) “Data Mining with Big Data”, IEEE Transactions on Knowledge and Data Engineering.
[11] Dean, J. and Ghemawat, S. 2008. MapReduce: simplified data processing on large clusters. Communication of ACM 51, 1 (Jan. 2008), 107-113.
[12] Agneeswaran VS, Tonpay P, Tiwary J (2013) Paradigms for realizing machine learning algorithms. Big Data 1(4):207–214.
[13] Laney D. 3D data management: controlling data volume, velocity, and variety, META Group, Tech. Rep. 2001.
[14] Vishal S Patil, Pravin D. Soni, “Hadoop skeleton & fault tolerance in hadoop clusters”, International Journal of Application or Innovation in Engineering & Management (IJAIEM)Volume 2, Issue 2, February 2013 ISSN 2319 - 4847
[15] Demchenko Y, Grosso P, de Laat C, Membrey P. Addressing big data issues in scientific data infrastructure. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, 2013. IEEE, pp 48–55.
[16] Dhruba, jssarma, jgray, kannan, Nicolas, hairong, krangana than dms, aravind, menon, rsh, Rodrigo, animated. “Apache HAdoop Goes Realtime at Facebook”.
[17] Cox M, Ellsworth D. Managing big data for scientific visualization. In: ACM Siggraph ?97 course 4 exploring gigabyte datasets in real-time: algorithms, data management, and time-critical design, August, 1997
[18] Bekkerman R, Bilenko M, Langford J. Scaling up machine learning: parallel and distributed approaches. Cambridge: Cambridge University Press; 2011.
[19] White T. Hadoop: The Definitive Guide, 3rd edn. Sebastopol, CA:O?Reilly Media, Inc.; 2012.
[20] Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, Saha B, Curino C, O?Malley O, Radia S, Reed B, Baldeschwieler E. Apache Hadoop : Yet Another Resource Negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing; 2013.
[21] Fernández A, del Río S, López V, Bawakid A, del Jesus MJ, Benítez JM, Herrera F. Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdiscip Rev Data Min Knowl Discov. 2014;4(5):380–409.
[22] Amir H. Payberah,? Introduction to Big Data -SICS?, April-8, 2014.
[23] Sandrine Dudoit and Robert Gentleman, „Introduction to Genome Biology?, 2003.
[24] A.Hammad, A.Garcia,?Hadoop tutorial?, September7, 2011.
[25] Suman Arora, Dr.Madhu Goel, “Survey Paper on Scheduling in Hadoop” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014
[26] Wang, F. et al. Hadoop High Availability through Metadata Replication. ACM (2009).
[27] Kaiser Permanente, in Oakland?16 Signs You May Have HIV?.
[28] Parmeshwari P. Sabnis, Chaitali A.Laulkar, “SURVEY OF MAPREDUCE OPTIMIZATION METHODS”, ISSN (Print): 2319- 2526, Volume -3, Issue -1, 2014
[29] LaValle et al: Big Data, Analytics and the Path From Insights to Value, (Dec 2010)
[30] International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at:Special Issue on 5th National Conference on Recent Trends in Information Technology 2016 Conference Held at P.V.P. Siddhartha Institute of Technology Kanuru, Vijayawada, India.
[31] Prashant Chauhan, Abdul Jhummarwala, Manoj Pandya, - Detection of DDoS Attack in Semantic Web| International Journal of Applied Information Systems (IJAIS) – ISSN: 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 4-No.6, December 2012
[32] A Review on HADOOP MAPREDUCE-A Job Aware Scheduling Technology ISSN(e): 2250 – 3005 Vol, 04 Issue, 5 May – 2014 International Journal of Computational Engineering Research (IJCER)
[33] Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang G-Z. Big data for health. IEEE J Biomed Health Inform. 2015;19(4):1193–208.
[34] Porambage P, et al. The quest for privacy in the internet of things. IEEE Cloud Comp. 2016;3(2):36–45.
[35] Acampora G, et al. Data analytics for pervasive health. In: Healthcare data analytics. ISSN:533-576. 2015.
[36] Spielman DA, Teng SH. A local clustering algorithm for massive graphs and its application to nearly-linear time graph partitioning 2008.
[37] Satyanarayana A. Intelligent sampling for big data using bootstrap sampling and chebyshev inequality. In: Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, 2014. pp 1–6.
[38] Yehia, Baligh R., Fleishman, John A., Metlay, Joshua P., et al. Comparing different measures of retention in outpatient HIV care. AIDS 2012, 26:1131-1139.
[39] Whitmore SK, Patel-Larson A, Espinoza L, et.al. Missed opportunities to prevent perinatal human immunodeficiency virus transmission in 15 jurisdictions in the United States during 2005-2008. Women Health 2010 Jul;50(5):414-25.
[40] Harrison KM, Kajese T, Hall HI, Song R. Risk factor redistribution of the national HIV/AIDS surveillance data: an alternative approach. Public Health Rep 2008;123:618–27.

Keywords
Big data, Hadoop, Cluster, AIDS.