Analysis and Application of Data Mining in CRM Systems of Healthcare Insurance
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2018 by IJCTT Journal | ||
| Volume-56 Number-1 |
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| Year of Publication : 2018 | ||
| Authors : Ms. Sheetal Macwan, Mr. Samrat Khanna | ||
| DOI : 10.14445/22312803/IJCTT-V56P104 |
Ms. Sheetal Macwan, Mr. Samrat Khanna "Analysis and Application of Data Mining in CRM Systems of Healthcare Insurance". International Journal of Computer Trends and Technology (IJCTT) V56(1):27-31, February 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Data mining techniques in Customer Relationship Management (CRM) has a very important role in the practice; it is an important means of gaining and maintaining customer information, and improving customer value. We have determined to compare a variety of techniques, approaches and different tools and its effect on the healthcare field. Data mining created new concept with customer relationship management where different companies can gain a reasonable advantage. The goal of data mining is to turn data into facts, figures, or text which can be processed by a computer into knowledge or information. The main reason of data mining application in healthcare systems is to create a mechanical tool for make out and distributes relevant healthcare information. The aim of this paper is to make a detailed study report of diverse types of data mining applications in the healthcare sector and to minimize the difficulty of healthcare data transactions. A relative study of different data mining applications, techniques and different methods applied for takeout knowledge from database produced in the healthcare industry.
References
[1] Kincade, K. (1998). Data mining: digging for healthcare gold. Insurance & Technology, 23(2), IM2-IM7
[2] Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), p. 44-47
[3] Christy, T. (1997). Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), p. 22-26.
[4] Newton’s Telecom Dictionary, Harry Newton, CMP Books, http://www.cmpbooks.com
[5] IDC & Cap Gemini. Four elements of customer relationship management. Cap Gemini White Paper.
[6] M. FreemanThe 2 customer lifecycles Intelligent Enterprise, 2 (16) (1999), p. 9
[7] Pilot Software. An introduction to data mining: discovering hidden value in your data warehouse. http://www3.shore.net/~kht/text/dmwhite/dmwhite.htm
[8] Information Discovery, Inc. A characterization of data mining technologies and processes: an Information Discovery, Inc. White Paper.
[9] Hand, D.J. (1998). Data mining: statistics and more? The American Statistician, 52(2), p. 112-118.
[10] Edelstein H. Data mining: exploiting the hidden trends in your data. DB2 Online Magazine. http://www.db2mag.com/9701edel.htm
[11] Milley, A. (2000). Healthcare and data mining. Health Management Technology, 21(8), p. 44-47
[12] Christy, T. (1997). Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), p. 22-26.
[13] Oakley, S. (1999). Data mining, distributed networks and the laboratory. Health Management Technology, 20(5), p. 26-31.
[14] Friedman, N.L. & Pliskin, N. (2002). Demonstrating value-added utilization of existing databases for organizational decision-support. Information Resources Management Journal, 15(4), p. 1-15.
[15] McQueen, G. & Thorley, S. (1999). Mining fool’s gold. Financial Analysts Journal, 55(2), p. 61-72.
[16] Cody, W.F. Kreulen, J.T. Krishna, V. & Spangler, W.S. (2002). The integration of business intelligence and knowledge management. IBM Systems Journal, 41(4), p. 697-713.
Keywords
Data Mining, healthcare system, healthcare industry, Customer Relationship Management (CRM).