University Of Tasmania
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Constructing an initial knowledge base for medical domain expert system using induct RDR

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conference contribution
posted on 2023-05-23, 12:02 authored by Hyeon, J, Oh, K-J, Kim, YJ, Hyunsuk ChungHyunsuk Chung, Byeong KangByeong Kang, Choi, H-J
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.


Publication title

Proceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp)


Institute of Electrical and Electronic Engineers, Inc






School of Information and Communication Technology


Curran Associates

Place of publication

Red Hook, New York, United States

Event title

2016 International Conference on Big Data and Smart Computing (BigComp)

Event Venue

Hong Kong, China

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2016 IEEE

Repository Status

  • Open

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified