Category Variable Selection Method for Efficient Clustering

  • Heo, Jun (Dept. of Information and Communication, Kyungmin University) ;
  • Kim, Chae Yun (Dept. of Medical IT Marketing, Eulji University) ;
  • Jung, Yong-Gyu (Dept. of Medical IT Marketing, Eulji University)
  • Received : 2013.09.01
  • Published : 2013.11.30


Recent medical industry is an aging society and the application of national health insurance, with state-of-the-art research and development, including the pharmaceutical market is greatly increased. The nation's health care industry through new support expansion and improve the quality of life for the research and development will be needed. In addition, systemic administration of basic medical supplies, or drugs are needed, the drug at the same time managing how systematic analysis of pharmaceutical ingredients, based on data through the purchase of new medicines and pharmaceutical ingredients automatically classified by analyzing the statistics of drug purchases and the future a system that can predict a patient is needed. In this study, the drugs to the patient according to the component analysis and predictions for future research techniques, k-means clustering and k-NN (Nearest Neighbor) Comparative studies through experiments using the techniques employ a more efficient method to study how to proceed. In this study, the effects of the drugs according to the respective components in time according to the number of pieces in accordance with the patient by analyzing the statistics by predicting future patient better medical industry can be built.


  1. O'Connor AM, Rostom A, Fiset V, et al. Decision aids for patients facing health treatment or screening decisions: systematic review. BMJ 1999;319:731-734.
  2. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artif Intell Med 2004;30(3):201-214. 202 Chapter 11
  3. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223- 1238.
  4. MandlKD, Szolovits P,Kohane IS. Public standards and patients' control: howto keep electronic medical records accessible but private. BMJ 2001;322(7281):283-287.
  5. Yong-Gyu Jung, Seung-Ho Lee and Ho Joong Sung, Effective Diagnostic Method Of Breast Cancer Data Using Decision Tree, Journal of IWIT (2010), Vol.10 No. 5 pp.57-62
  6. Yong-Gyu Jung , Jun Heo, Kyu Ho kim, Using Discretization of Numeric Attributes to Compare the Changes in Performance of C4.5 and CART algorithms, International Conference of the Korea Distribution Science Association, ISSN2287-478X Vol.4 pp353-358 2013.7.11,