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A Belief Rule based Expert System to Diagnose Dengue Fever under Uncertainty
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
University of Chittagong.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2017 (English)In: Proceedings of Computing Conference 2017 / [ed] Liming Chen, Nikola Serbedzija, Kami Makki, Nazih Khaddaj Mallat, Kohei Arai, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 179-186Conference paper, Published paper (Refereed)
Abstract [en]

Dengue Fever is a debilitating mosquito-borne disease, causing sudden fever, leading to fatality in many cases. A Dengue patient is diagnosed by the physicians by looking at the various signs, symptoms and risk factors of this disease. However, these signs, symptoms and the risk factors cannot be measured with 100% certainty since various types of uncertainties such as imprecision, vagueness, ambiguity, and ignorance are associated with them. Hence, it is difficult for the physicians to diagnose the dengue patient accurately since they don’t consider the uncertainties as mentioned. Therefore, this paper presents the design, development and applications of an expert system by incorporating belief rule base as the knowledge representation schema as well as the evidential reasoning as the inference mechanism with the capability of handling various types of uncertainties to diagnose dengue fever. The results generated from the expert system are more reliable than from fuzzy rule based system or from human expert.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 179-186
National Category
Natural Sciences Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-64892DOI: 10.1109/SAI.2017.8252101ISI: 000426944400023Scopus ID: 2-s2.0-85046017063ISBN: 978-1-5090-5442-8 (electronic)ISBN: 978-1-5090-5443-5 (electronic)OAI: oai:DiVA.org:ltu-64892DiVA, id: diva2:1127778
Conference
Computing Conference 2017, London, 18-20 July 2017
Projects
BRBWSN
Funder
Swedish Research Council, 2014-4251Available from: 2017-07-19 Created: 2017-07-19 Last updated: 2018-05-07Bibliographically approved

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Hossain, Mohammad ShahadatAndersson, Karl

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