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An Intelligent System to Diagnose Chikungunya under Uncertainty
University of Chittagong, Bangladesh.ORCID-id: 0000-0002-7473-8185
International Islamic University Chittagong, Bangladesh.
International Islamic University Chittagong, Bangladesh.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. (Mobile and Pervasive Computing)ORCID-id: 0000-0003-0244-3561
2019 (Engelska)Ingår i: Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, ISSN 2093-5374, E-ISSN 2093-5382, Vol. 10, nr 2, s. 37-54Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Chikungunya is a virus-related disease, bring about by the virus called CHIKV that spreads throughmosquito biting. This virus first found in Tanzania, while blood from patients was isolated. Thecommon signs and symptoms, associated with Chikungunya are considered as fever, joint swelling,joint pain, muscle pain and headache. The examination of these signs and symptoms by the physician constitutes the typical preliminary diagnosis of this disease. However, the physician is unable tomeasure them with accuracy. Therefore, the preliminary diagnosis in most of the cases could sufferfrom inaccuracy, which leads to wrong treatment. Hence, this paper introduces the design and implementation of a belief rule based expert system (BRBES) which is capable to represent uncertainknowledge as well as inference under uncertainty. Here, the knowledge is illustrated by employing belief rule base while deduction is carried out by evidential reasoning. The real patient data of250 have been considered to demonstrate the accuracy and the robustness of the expert system. Acomparison has been performed with the results of BRBES and Fuzzy Logic Based Expert System(FLBES) as well as with the expert judgment. Furthermore, the result of BRBES has been contrastedwith various data-driven machine learning approaches, including ANN (Artificial Neural networks)and SVM (Support Vector Machine). The reliability of BRBESs was found better than those of datadriven machine learning approaches. Therefore, the BRBES presented in this paper could enable thephysician to conduct the analysis of Chikungunya more accurately.

Ort, förlag, år, upplaga, sidor
Korea: JoWUA , 2019. Vol. 10, nr 2, s. 37-54
Nyckelord [en]
Belief Rule Base, Uncertainty, Evidential Reasoning, Expert System, Chikungunya
Nationell ämneskategori
Datavetenskap (datalogi) Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-75300DOI: 10.22667/JOWUA.2019.06.30.037OAI: oai:DiVA.org:ltu-75300DiVA, id: diva2:1337393
Projekt
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Forskningsfinansiär
Vetenskapsrådet, 2014-4251
Anmärkning

Validerad;2019;Nivå 1;2019-08-14 (johcin)

Tillgänglig från: 2019-07-14 Skapad: 2019-07-14 Senast uppdaterad: 2019-08-14Bibliografiskt granskad

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Hossain, Mohammad ShahadatAndersson, Karl
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Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Datavetenskap (datalogi)Medieteknik

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