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204     Abdulrahman Albar, Ahmad Elshennawy, Mohammed Basingab et al.

                            The  expert  has  deep  experience  in  the  daily  operations  of  emergency  care
                              centers.
                            The expert has solid knowledge in staffing, performance management, healthcare
                              administration, patient flow analysis, and bed management.

                          To create a robust knowledge base for the proposed fuzzy system, a minimum of ten
                       experts are required who meet these qualifications. While discussing these experts here
                       for the purposes of analyzing their data, and elsewhere in this study, an assigned code
                       “HCE-k” will be issued for each participated expert, where HCE stands for Healthcare
                       Expert, and k stands for the expert number.


                       Database

                          This study adapts the indirect interval estimation elicitation method. Such a method
                       carries advantages such as allowing responses from multiple subject matter experts, while
                       not  requiring  knowledge  of  membership  functions.  Additionally,  under  this  approach,
                       fewer questions may be used, and given questions may be easier to answer than those in
                       other  approaches.  To  elicit  the  degrees  of  membership  for  a  fuzzy  class,  let  [      ,]
                       represent the interval values of the fuzzy class j that is determined by expert i. The steps
                       to elicit and analyze expert knowledge are described as follows:

                          - Determine all interval values for each j obtained from experts.
                          - Perform an intersection for j subset intervals to obtain expert consensus.
                          - Find ambiguous areas among determined intervals.


                       Fuzzy Rule Base

                          The  fuzzy  rule  base  is  the  other  key  part  to  the  knowledge  base,  including  the
                       database.  It  stores  all  derived  fuzzy  rules,  which  is  intended  to  provide  the  fuzzy
                       inference engine with decision support information within each subsystem. To robustly
                       create fuzzy rules for each fuzzy logic subsystem, experts are given a form to assess the
                       consequences of each condition statement, developed from the permutation of each fuzzy
                       class for a given fuzzy subsystem. A total of 10 healthcare experts will participate in the
                       fuzzy  rules  assessment  process.  The  total  number  of  fuzzy  rules  to  be  evaluated  by
                       subject  matter  experts  for  the  fuzzy  logic  subsystems  I,  II,  III,  and  IV  are  12  (3×4),
                                                    2
                       9(3 ),  36(4×3 ),  and  80(5×4 ),  respectively.  Therefore,  the  proposed  three-level
                          2
                                     2
                       hierarchical fuzzy expert system includes a total of 137 fuzzy rules, meaning that there
                       will  be  a  total  of  1370  fuzzy  rule  assessments  from  the  ten  experts.  The  process  of
                       developing the fuzzy rules is detailed in the following steps:
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