Comparison of Alternative knowledge Acquisition Methods for Allergic Rhinitis

  • Chae, Young-Moon (Graduate School of Health Science and Management, Yonsei University) ;
  • Chung, Seung-Kyu (Department of Otolaryngology, Samsung Medical Center) ;
  • Suh, Jae-Gwon (Graduate School of Health Science and Management, Yonsei University) ;
  • Ho, Seung-Hee (Graduate School of Health Science and Management, Yonsei University) ;
  • Park, In-Yong (Department of Otolaryngology4, Younsei University of College of Medicine)
  • Published : 1995.01.01

Abstract

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).