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Managing Overcrowding in Healthcare using Fuzzy Logic 203

Figure 8: Fuzzy logic subsystem III. Figure 9: Fuzzy logic subsystem IV.

FUZZY LOGIC SYSTEM DEVELOPMENT

This section describes the technical process of developing the proposed fuzzy expert
system, which would equip the designed framework with a knowledge base, a fuzzy
inference engine, fuzzifier and defuzzifier. The knowledge base consists of a fuzzy
database and a fuzzy rule base, in order to fuel the fuzzifier, defuzzifier, and inference
engine portions of the fuzzy subsystems.
First, the elicitation of expert knowledge for building the fuzzy database is described.
Secondly, this section also describes the process of developing fuzzy rules. Finally, the
fuzzification and the defuzzification processes are conceptually and mathematically
represented.

Knowledge Base

The knowledge base is an indispensable component of any fuzzy logic system, as it
contains both the fuzzy rules base and the database. The development of the knowledge
base is keystone for the fuzzy system, and is the most challenging aspect of designing the
proposed model. The importance of this knowledge base stems from the dependency of
the other component of the system on it, including the fuizzifier, defuzzifier, and fuzzy
inference engine. Effectively, the knowledge base is the brain of the fuzzy system,
simulating reasoning from a human perspective. The creation of the knowledge base
involves systematic collection of qualitative and quantitative data from subject matter
experts. These experts have to meet the following criteria in order to be eligible to
participate in the membership intervals determination and fuzzy rules evaluation:

 The expert works or has recently worked in Saudi Arabia healthcare institutions
for at least five years, or has conducted research in the field of Saudi healthcare.