Abstract

Big data related to large volume, multiple ways of growing data sets and autonomous sources. Now the big data is quickly enlarged in many advanced domains, because of rapid growth in networking and data collection. The study is defining the E-Healthcare Information System, which needs to make logical and structural method of approaching the knowledge. And also effectually preparing and controlling the data generated during the diagnosis activities of medical application through sharing information among E-Healthcare Information System devices. The main objective is, A E-Healthcare Information System which is extensive, integrated knowledge system designed to control all the views of a hospital operation, such as medical data’s, administrative, financial, legal information’s and the corresponding service processing. At last the analysis of result will be generated using Association Mining Techniques which processed from big data of hospital information datasets. Finally mining techniques result could be evaluated in terms of accuracy, precision, recall and positive rate.

Keywords:

Association rule mining, autonomous sources , e-healthcare information system , information sharing, medical diagnosis , medical knowledge,

References

1. Birney, E., 2012. The making of ENCODE: Lessons for big data projects. Nature, 489: 49-51.
   CrossRef    PMid:22955613    
2. Chang, E.Y., H. Bai and K. Zhu, 2009. Parallel algorithms for mining large-scale rich-media data. Proceedings of the 17th ACM International Conference on Multimedia (MM '09). New York, USA, pp: 917-918.
   CrossRef    
3. Chu, C.T., S.K. Kim, Y.A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng and K. Olukotun, 2006. Map-reduce for machine learning on multicore. Proceeding of the 20th Annual Conference on Neural Information Processing Systems (NIPS'06), pp: 281-288.
   PMid:16780672    
4. IBM, 2012. What is Big Data: Bring Big Data to the Enterprise. IBM.Retrieved from: http://www-01.ibm.com/software/data/bigdata/.
   Direct Link
5. Koteeswaran, S. and E. Kannan, 2012. Terrorist intrusion monitoring system using outlier analysis based search knight algorithm. Eur. J. Sci. Res., 74(3): 440-449.
   
6. Koteeswaran, S. and E. Kannan, 2013. Analysis of Bilateral Intelligence (ABI) for textual pattern learning. Inform. Technol. J., 12(4): 867-870.
   CrossRef    
7. Labrinidis, A. and H. Jagadish, 2012. Challenges and opportunities with big data. Proc. VLDB Endowment, 5(12): 2032-2033.
   CrossRef    
8. Machanavajjhala, A. and J.P. Reiter, 2012. Big privacy: Protecting confidentiality in big data. ACM Crossroads, 19(1): 20-23.
   CrossRef    
9. Michel, F., 2012. How Many Photos are Uploaded to Flickr Every Day and Month? Retrieved from: http://www.flickr.com/photos/franckmichel/6855169886/.
   Direct Link
10. Punnagai, N., K. Ayarpadi, C. Leena and S. Koteeswaran, 2011. Simulation of broadcasting algorithm using neighbor information in mobile ad hoc networks. Proceeding of the IET International Conference on Sustainable Energy and Intelligent Systems (SEISCON, 2011). Tamilnadu, India, pp: 908-912.
    
11. Ranger, C., R. Raghuraman, A. Penmetsa, G. Bradski and C. Kozyrakis, 2007. Evaluating MapReduce for multi-core and multiprocessor systems. Proceeding of the 13th IEEE International Symposium on High Performance Computer Architecture (HPCA'07), pp: 13-24.
    CrossRef    
12. Reed, C., D. Thompson, W. Majid and K. Wagstaff, 2011. Real time machine learning to find fast transient radio anomalies: A semi-supervised approach combining detection and RFI excision. Proceeding of the International Astronomical Union Symposium on Time Domain Astronomy, UK.
    
13. Su, K., H. Huang, X. Wu and S. Zhang, 2006. A logical framework for identifying quality knowledge from different data sources. Decis. Support Syst., 42(3): 1673-1683.
    CrossRef    
14. Wu, X. and S. Zhang, 2003. Synthesizing high-frequency rules from different data sources. IEEE T. Knowl. Data En., 15(2): 353-367.
    CrossRef    
15. Wu, X., C. Zhang and S. Zhang, 2005. Database classification for multi-database mining. Inform. Syst., 30(1): 71-88.
    CrossRef    
16. Wu X., K. Yu, W. Ding, H. Wang and X. Zhu, 2013. Online feature selection with streaming features. IEEE T. Pattern Anal., 35(5): 1178-1192.
    CrossRef    PMid:23520258    
17. Xindong, W., Z. Xingquan, W. Gong-Qing and D. Wei, 2014. Data mining with big data. IEEE T. Knowl. Data En., 26(1): 97-107.
    CrossRef    
18. Zhu, X., P. Zhang, X. Lin and Y. Shi, 2010. Active learning from stream data using optimal weight classifier ensemble. IEEE T. Syst. Man Cy. B, 40(6): 1607-1621.
    CrossRef    PMid:20363683    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.