2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Jonghwan Hyeon , School of Computing, KAIST, Daejeon, Republic of Korea
Kyo-Joong Oh , School of Computing, KAIST, Daejeon, Republic of Korea
You Jin Kim , School of Computing, KAIST, Daejeon, Republic of Korea
Hyunsuk Chung , School of Engineering and ICT, University of Tasmania, Hobart, Australia
Byeong Ho Kang , School of Engineering and ICT, University of Tasmania, Hobart, Australia
Ho-Jin Choi , School of Computing, KAIST, Daejeon, Republic of Korea
This paper describes how we build an initial knowledge-base of ripple-down rules (RDR) in medical domain. In medical domain, all decisions are made by the domain experts. Increasing a complexity of disease and various symptoms, there are some attempts to introduce an expert system in medical domain these days. To construct the expert system, it needs to extract the expert's knowledge. To do that, we use ripple-down rules (RDR) which allows experts to modify their knowledge base directly because it provides a systematic approach to do that. We also use Induct RDR which builds a knowledge base from existing data to reduce experts' burden of adding their knowledge from the bottom up. The expert system should produce multiple comments from a test set, which is multiple classification problem. However, Induct RDR only deals with a single classification problem. To handle this problem, we divide a test set into 18 categories which is almost the single classification problem and apply Induct RDR to each category independently. Using this approach, we can improve the missing rate about 70% compared to an approach not dividing into several categories.
Expert systems, Medical diagnostic imaging, Knowledge engineering, Diseases, Systematics, Maintenance engineering
Jonghwan Hyeon, Kyo-Joong Oh, You Jin Kim, Hyunsuk Chung, B. H. Kang and Ho-Jin Choi, "Constructing an initial knowledge base for medical domain expert system using induct RDR," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 408-410.