Abstract

STUDY ON ROLE OF DATA MINING IN HEALTH CARE

Simerjit Kaur, Indu Singh

047-052

Vol: 1, Issue: 2, 2011

The role of data mining in health care has become the subject matter of wide and varied research activities [Kaur H. et.al. 2006]. Extraction of useful information from health care data offers a lot of challenges in terms of storage, dissemination, privacy and security of patient data. While the privacy issue is more of a legal and ethical issue rather than technological issue, data mining offers broader communitybased gains that enable and improve healthcare forecasting, analysis, and visualisation [Payton F.C., 2003]. Guided use of technologies like database systems, data mining and knowledge management can contribute a lot to decision support systems in health care.

Download PDF

    References

  1. Abe H., Yokoi H., Ohsaki M. and Yamaguchi, T. (2007). Developing an Integrated Time-Series Data Mining Environment for Medical Data Mining. Seventh IEEE International Conference on Data Mining, 28-31 Oct. 2007, 127-132.
  2. Agrawal R. and Srikant R. (1994). Fast Algorithms for Mining Association Rule. Proceedings of the 20th International Conference on Very Large Databases (VLDB), 487 – 499.
  3. Ankerest M., Ester M. and Kriegel H.P. (2000). Towards an Effective Cooperation of the User and the Computer for Classification. Proceedings of 6th International conference on Knowledge Discovery and Data Mining, Boston, MA.
  4. Bates J.H.T. and Young M.P. (2003). Applying Fuzzy Logic to Medical Decision Making in the Intensive Care Unit. American Journal of Respiratory and Critical Care Medicine, Vol. 167, 948-952.
  5. Berks G., Keyserlingk D.G.V., Jantzen J., Dotoli M. and Axer H. (2000). Fuzzy Clustering - A Versatile Mean to Explore Medical Databases. ESIT, Aachen, Germany, 453-457.
  6. Berson A., Smith S. and Thearling K. (1999). Building Data Mining Applications for CRM. First Edition, McGraw-Hill Professional.
  7. Bethel C.L., Hall L.O. and Goldgof D. (2006). Mining for Implications in Medical Data. Proceedings of the 18th International Conference on Pattern Recognition,Vol.1, 1212-1215.
  8. Cheung Y.M. (2003). k-Means: A New Generalised k-Means Clustering Algorithm. N-H Elsevier Pattern Recognition Letters 24, Vol 24(15), 2883–2893.
  9. Chiang I.J., Shieh M.J., Hsu J.Y.J. and Wong J.M. (2005). Building a Medical
  10. Frank H., Klawonn F., Kruse R. and Runkler T. (1999). Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. New York: John Wiley.
  11. Frawley W.J., Piatetsky-Shapiro G. and Matheus C.(1996). Knowledge Discovery in Databases: An Overview. Knowledge Discovery in Databases, AAAI Press/MIT Press, Cambridge, MA., Menlo Park, C.A, 1-30.
  12. Houtsma M.A.W. and Swami A.N. (1993). Set-Oriented Mining for Association Rules in Relational Databases. Proceedings of the Eleventh International Conference on Data Engineering, 25-33.
  13. Leung, K.S., Lee K.H., Wang J.F., Ng E. YT, Chan H. LY, Tsui S. KW, Mok T. SK, Tse P.C.H. and Sung J. J.Y.(2009). Data Mining on DNA Sequences of Hepatitis B Virus. IEEE/ACM Transactions on Computational Biology and Bioinformatics. IEEE computer Society Digital Library.
  14. Liu S-H., Chang K-M. and Tyan C-C. (2008). Fuzzy C-Means Clustering for Myocardial Ischemia Identification with Pulse Waveform Analysis. 13th International Conference on Biomedical Engineering, Singapore, Vol. 23, 485-489.
  15. Marx K.A., O'Neil P., Hoffman P. and Ujwal M.L. (2003). Data Mining the NCI Cancer Cell Line Compound GI (50) Values: Identifying Quinine Subtypes Effective against Melanoma and Leukemia Cell Classes. United-States: Journal of Chemical Information and Computer Sciences, Vol. 43, 1652-1667.
  16. Match-Project: http://www.match-project.com/
  17. Mounji, A. (1997). Languages and Tools for Rule-Based Distributed Intrusion Detection. PhD thesis, Faculties Universitaires Notre-Dame dela Paix Namur (Belgium).
  18. Pace R.K. and Zou D. (2000). Closed-Form Maximum Likelihood Estimates of Nearest Neighbor Spatial Dependence. Geoghraphical Anaylsis, Vol. 32(2).
  19. Pechenizkiy M. Tsymbal A. and Puuronen S. (2005). Knowledge Management Challenges in Knowledge Discovery Sytems. 16th IEEE International Workshop on Database and Expert Systems Applications, 433-437.
  20. Pei J., Upadhyaya S.J., Farooq F. and Govindaraju V. (2004). Data Mining for Intrusion Detection: Techniques, Applications and Systems. Proceedings of the 20th International Conference on Data Engineering, p.877.
  21. Rahm E. and Do H. H. (2000). Data Cleaning: Problems and Current Approaches. IEEE Bulletin on Data Engineering, Vol. 23(4).
  22. Saeed M., Lieu C., Raber G. and Mark R.G. (2002). MIMIC: A Massive Temporal ICU Patient Database to Support Research in Intelligent Patient Monitoring. IEEE Computers in Cardiology, Vol. 29, 641-44.
  23. Selfridge P. and SrivastvaD. (1996). A Visual Language for Interactive Data Exploration and Analysis. Proceedings of the 1996 IEEE Symposium on Visual Languages, 84.
  24. Soukup T. and Davidson Ian. (2002). Visual Data Mining: Techniques and Tools for Data Visualisation and Mining. Wiley Dreamtech India Pvt. Ltd. First Edition 2002.Soukup T. and Davidson Ian. (2002). Visual Data Mining: Techniques and Tools for Data Visualisation and Mining. Wiley Dreamtech India Pvt. Ltd. First Edition 2002.
  25. Srikant R., Vu Q. and Agrawal R. (1997). Mining Association Rules With Item Constraints. Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining.
  26. The official web site of Central Beauro of Health Intelligence: http://www.cbhidghs.nic.in
  27. Ye N. and Li X. (2003). Application of Decision Tree Classifiers to Computer Intrusion Detection. Real-Time System Security, 77 – 93.
  28. Zhang S., Liu S., Wang D., Ou J. and Wang G. (2006). Knowledge Discovery of Improved Apriori-Based High-Rise Structure Intelligent Form Selection. Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, Vol.1, 535-539.
Back

Disclaimer: Indexing of published papers is subject to the evaluation and acceptance criteria of the respective indexing agencies. While we strive to maintain high academic and editorial standards, International Journal of Research in Science and Technology does not guarantee the indexing of any published paper. Acceptance and inclusion in indexing databases are determined by the quality, originality, and relevance of the paper, and are at the sole discretion of the indexing bodies.