Mohd Sharif, Nurul Atikah and Ahmad, Norazura and Ahmad, Nazihah and Mat Desa, Wan Laailatul Hanim and Helmy, Khaled Mohamed and Wei, Chern Ang and Zainol Abidin, Ida Zaliza (2019) A fuzzy rule-based expert system for asthma severity identification in emergency department. Journal of Information and Communication Technology (JICT), 18 (4). pp. 415-438. ISSN 1675-414X

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Abstract

The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively manages the fuzziness of the patient’s information data, and determines the subjective judgment of medical practitioners’ level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia.System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches.

Item Type:	Article
Uncontrolled Keywords:	Emergency department, acute asthma, fuzzy rule-based.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Quantitative Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	01 Mar 2020 00:56
Last Modified:	01 Mar 2020 00:56
URI:	https://repo.uum.edu.my/id/eprint/26845

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