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Reseach Article

Mining Health Data using Weighted Approach

by P. Priyanga, Naveen N. C.
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 10
Year of Publication: 2016
Authors: P. Priyanga, Naveen N. C.

P. Priyanga, Naveen N. C. . Mining Health Data using Weighted Approach. Communications on Applied Electronics. 5, 10 ( Sep 2016), 1-6. DOI=10.5120/cae2016652381

@article{ 10.5120/cae2016652381,
author = { P. Priyanga, Naveen N. C. },
title = { Mining Health Data using Weighted Approach },
journal = { Communications on Applied Electronics },
issue_date = { Sep 2016 },
volume = { 5 },
number = { 10 },
month = { Sep },
year = { 2016 },
issn = { 2394-4714 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/cae2016652381 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:55:10.619418+05:30
%A P. Priyanga
%A Naveen N. C.
%T Mining Health Data using Weighted Approach
%J Communications on Applied Electronics
%@ 2394-4714
%V 5
%N 10
%P 1-6
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Web Analytics (WA) is a vital area in the field of Data Mining (DM) that works with the principle of extracting interesting information or knowledge from the World Wide Web. WA is the measurement, collection, analysis and reporting of Internet data. The research in WA has led to the development of new techniques to generate automated topic hierarchies and web dictionaries. WA plays a major role in health care domain, to search health related information required from the web. Gathering knowledge about health has become a complex procedure for the majority of users. This confuses the users and consuming more time in overloaded data that continue to enlarge. Applications of DM to Web-page ranking helps Web search engines to find high quality web pages. In this paper Machine Learning (ML) methods for extracting knowledge from the large medical data on the Internet which is heterogeneous in nature of the web is proposed. The main objective is to develop a fast and efficient algorithm for real-time processing of big data and create knowledge out of the existing information in the web.

  1. C. Sherman. Curing Medical Information Disorder., 2005.
  2. 'Googling' Aids Difficult Diagnoses., 2006.
  3. M. Klein, H. Easley. Checking Medical Facts Online can be OK, but don't Become a 'Cyberchondriac'. The Journal News, June 26, 2006. 6/NEWS03/606260311/1019.
  4. Healthline homepage.
  5. Kosala and Blockeel, “ Web mining research: a survey, “ SIGKDD: SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining, ACM, Vol.2, 2000.
  6. A.Z. Broder. Identifying and Filtering Near-Duplicate Documents. CPM 2000: 1-10.
  7. J.G. Carbonell, J. Goldstein. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR 1998: 335-336.
  8. C. Zhai, W.W. Cohen, and J.D. Lafferty. Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. SIGIR 2003: 10-17.
  9. B. Zhang, H. Li, and Y. Liu et al. Improving Web Search Results Using Affinity Graph. SIGIR 2005: 504-511.
  10. The National Coalition on Health Care. Facts on the Cost of Health Care., 2006.
  11. O. etzioni, The world wield web: Quagmire or Gold Mining. Communicate of the ACM, (39)11:65-68, 1996.
  12. Chen, M., Park, J. and Yu, P. “Efficient data mining for path traversal patterns,” in IEEE Transactions on knowledge and data engineering, Volume 10, No.2, March/April 1998, 209-221.
  13. Mannila, H. and Toivonen, H. “Discovering generalized episodes using minimal occurrences,” in International Conference on Knowledge and Data Mining, 1996, 146-151.
  14. Yan. T., Jacobsen. M, Garcia-Molina. H, Dayal. U, “From user access patterns to dynamic hypertext linking,” in International World Wide Web conference on Computer networks and ISDN systems, Volume 28 Issue 7-11, Pages 1007-1014, 1996.
  15. Jasleen Kaur Bains, “Big Data Analytics in Healthcare- Its Benefits, Phases and Challenges,” in International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 4, April 2016. ISSN: 2277 128X
  16. Bharati Suvalka ,Sarika kandelwal , Sidharth Singh Sisodia, “Big Data Analytics using Meta Machine Learning,” International Journal of Innovative Research in Science, Engineering and Technology , Vol. 3, Issue 8, August 2014. ISSN: 2319-8753
  17. Dalia AbdulHadi AbdulAmeer, “Medical Data Mining: Health Care Knowledge Discovery Framework Based On Clinical Big Data Analysis,” International Journal of Scientific and Research Publications, Volume 5, Issue 7, July 2015. ISSN 2250-3153.
  18. Gang Luo, Chunqiang Tang, Hao Yang, Xing Wei, “MedSearch: A Specialized Search Engine for Medical Information Retrieval”, CIKM’08, October 26–30, 2008.
  19. Mayank Trivedi, “A study of search engines for health sciences”, International Journal of Library and Information Science Vol. 1(5) pp. 069-073 October, 2009.
  20. Shubham Borikar, Mohan Bhagchandani, Raunak Kochar, Ketansing Pardeshi, Manisha Gahirwal, “ A Survey on Applications of Big Data Analytics in Healthcare” , International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-5 Issue-5, November 2015.
  21. Priyanka K, Prof Nagarathna Kulennavar, “ A Survey On Big Data Analytics In Health Care”, International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5865-5868. ISSN 0975-9646.
  22. Gemson Andrew Ebenezer J and Durga S , “ Big data analytics in healthcare: a survey”, ARPN Journal of Engineering and Applied Sciences , V0l. 10, No. 8, May 2015. ISSN 1819-6608.
  23. Google Health homepage. Curbside.MD homepage., 2008.
  24. SearchMedica - The GPs search engine., 2006.
  25. Medstory homepage.
  26. Erik Cambria, Guang-Bin Huang, “Extreme Learning Machines,” Published by the IEEE Computer Society, Nov/Dec 2013.
Index Terms

Computer Science
Information Sciences


Web Analytics; Health care; Big Data; Machine Learning