analysis of current applications and issues of data mining in healthcare
A.Vanitha,N.Nagadeepa
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:3 Issue:3 Year: 25 October,2014 Pages:375-383
Abstract
The application of data mining in the fields like e-business, marketing and retail are successful. They are popular by its use in knowledge discovery in databases (KDD) in other industries and sectors. Among these sectors that are just discovering data mining are the fields of medicine and public health. This research paper focuses on the survey of current techniques of KDD, using data mining tools for healthcare and public health. It also discusses critical issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and pre vendible hospital deaths, and detection of fraudulent insurance claims.
Kewords
Adverse drug events,non-invasive, painless way
Reference
Audain, C. (2007). Florence Nightingale. On-line: http://www.scottlan.edu/lriddle/women/ nitegale.htm. Accessed 30 July 2009. Ayres, I (2008). Super Crunchers. New York: Bantam Books. Shillabeer, A. and Roddick, J (2007). Estab-lishing a Lineage for Medical Knowledge Discovery. ACM International Conference Proceeding Series. (311) 70, 29-37. Bailey-Kellog, C. Ramakrishnan, N. and Marathe, M. Spatial Data Mining to Support Pandemic Preparedness. SIGKDD Explorations (8) 1, 80-82. Cao, X., Maloney, K.B. and Brusic, V. (2008). Data mining of cancer vaccine trials: a bird's-eye view. Immunize Research, 4:7. DOI:10.1186/1745-7580-4-7 Cheng, T.H., Wei, C.P., Tseng, V.S. (2006) Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Auto-matic Approaches. Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06). Health Grades, Inc. (2007). The Fourth Annual Health Grades Patient Safety in American Hospitals Study. Kou, Y., Lu, C.-T., Sirwongwattana, S., and Huang, Y.-P. (2004). Survey of fraud detection techniques. In Networking, Sensing and Control, 2004 IEEE International Conference on Networking, Sensing and Control. (2) 749-754. Nightingale, F (1858). Notes on Matters Affecting the Health, Efficiency and Hospital Administration of the British Army. Shillabeer, A (29 July 2009). Lecture on Data Mining in the Health Care Industry. Carnegie Mellon University Australia. Tandoc, E.S (14 October 2006). DOH order probe after Rizal hospital tragedy -- Sanitation regulations stressed. Philippine Daily Inquirer, p. A19. Thangavel, K., Jaganathan, P.P. and Easmi, P.O. Data Mining Approach to Cervical Cancer Patients Analysis Using Clustering Technique. Asian Journal of Information Technology (5) 4, 413-417. Tufte, E. (1997). Visual Explanations. Images And Quantities, Evidence and Narrative. Connecticut: Graphics Press. Wong, W.K., Moore, A., Cooper, G. and Wagner, M (2005). What's Strange About Re-cent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks? Journal of Machine Learning Research. 6, 1961-1998. Wilson A., Thabane L., Holbrook A (2003). "Application of data mining techniques in pharmacovigilance". British Journal of Clinical Pharmacology. (57) 2, 127-134. Witten, I. H. and Frank, E. (2005). Data mining: practical machine learning tools and techniques. Morgan Kaufmann series in data management systems. Morgan Kaufman
