scholarly journals A Fuzzy Rule-Based Expert System to Determine Propofol Drug Dosage in Anesthesia

2021 ◽  
Vol 10 (1) ◽  
pp. 89
Author(s):  
Melika Babaei ◽  
Sharareh R. Niakan Kalhori ◽  
Shima Sheybani ◽  
Hesam Karim
Keyword(s):  
Expert System ◽  
Mean Squared Error ◽  
Initial Dosage ◽  
Fuzzy Rule ◽  
Drug Dosage ◽  
T Test ◽  
Rule Based ◽  
Anesthetic Drugs ◽  
Linguistic Rules ◽  
Paired T Test

Introduction: Inadequate anesthetic, including under or over dosage, may lead to intraoperative awareness or prolonged recovery. Fuzzy expert systems can assist anesthesiologist to manage drug dosage in a right manner. Designing a fuzzy rule-based expert system to determine the Propofol anesthetic drug dosage was the main objective of this study.Material and Methods: This is a retrospective study. Fuzzy IF-THEN rules were defined based on evidences and experts’ linguistic rules for Propofol dose determination. Fuzzy toolbox in MATLAB software was used to design the system. Validation of system conducted with calculation of mean absolute error (MAE) and root mean squared error (RMSE). Also, difference mean between actual and predicted doses was tested with paired t-test in SPSS V.26 software. Data from 50 ENT (ears, nose, and throat) surgeries were used to validate the fuzzy system.Results: MAE for induction and maintenance doses was 0.128 and 1.95 respectively. RMSE for induction and maintenance doses was 0.228 and 3.383 respectively. Based on paired t-test result, there was no significant correlation between actual and predicted values (P>0.05).Conclusion: Obtained value from test and validation of system demonstrated a high performance and satisfying accuracy of the system. Therefore, this expert system can be used as a decision support system to determine initial dosage of anesthetic drugs. It can also be used for anesthesia students to learn drug administration.

scholarly journals Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

2020 ◽  
Vol 10 (2) ◽  
pp. 611 ◽  
Author(s):  
Serkan Örücü ◽  
Murat Selek
Keyword(s):  
Expert System ◽  
Real Time ◽  
Support System ◽  
T Test ◽  
Rule Base ◽  
Test Analysis ◽  
Rule Based ◽  
Kinect V2 ◽  
Movement Models ◽  
Paired T Test

In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.

scholarly journals A FUZZY RULE-BASED EXPERT SYSTEM FOR ASTHMA SEVERITY IDENTIFICATION IN EMERGENCY DEPARTMENT

2019 ◽  
Vol 18 (4) ◽  
pp. 415-438
Author(s):  
Nurul Atikah Mohd Sharif ◽  
Norazura Ahmad ◽  
Nazihah Ahmad ◽  
Wan Laailatul Hanim Mat Desa ◽  
Khaled Mohamed Helmy ◽  
...  
Keyword(s):  
Emergency Department ◽  
Expert System ◽  
Fuzzy Rule ◽  
Asthma Severity ◽  
Rule Based

scholarly journals A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Neda Mohammadhasan ◽  
Susan Bastani
Keyword(s):  
Performance Evaluation ◽  
Expert System ◽  
Intellectual Capital ◽  
Fuzzy Rule ◽  
Qualitative Evaluation ◽  
Linguistic Variables ◽  
Rule Based ◽  
Proposed Model ◽  
Linguistic Approach ◽  
Fuzzy Linguistic

A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.

scholarly journals FESAEI: a fuzzy rule-based expert system for the assessment of environmental impacts

2018 ◽  
Vol 190 (9) ◽  
Author(s):  
José E. de Tomas Sánchez ◽  
Sergio de Tomás Marín ◽  
Victoriano Peiró Clavell
Keyword(s):  
Expert System ◽  
Environmental Impacts ◽  
Fuzzy Rule ◽  
Rule Based

Fuzzy rule based expert system for diagnosis of multiple sclerosis

2014 ◽  
Author(s):  
M. Arabzadeh Ghahazi ◽  
M. H. Fazel Zarandi ◽  
M. H. Harirchian ◽  
S. Rahimi Damirchi-Darasi
Keyword(s):  
Multiple Sclerosis ◽  
Expert System ◽  
Fuzzy Rule ◽  
Rule Based

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

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