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A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
University of Chittagong, Bangladesh.
University of Chittagong, Bangladesh.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
Number of Authors: 4
2017 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, no 3, 43Article in journal (Refereed) Published
Abstract [en]

The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100\% certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule based expert system. 

Place, publisher, year, edition, pages
2017. Vol. 41, no 3, 43
Keyword [en]
Expert System, Belief Rule Base, Uncertainty, Tuberculosis, Signs and Symptoms
National Category
Computer Science Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-61319DOI: 10.1007/s10916-017-0685-8ISI: 000395075100007PubMedID: 28138886OAI: oai:DiVA.org:ltu-61319DiVA: diva2:1062816
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Note

Validerad; 2017; Nivå 2; 2017-01-31 (andbra)

Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2017-10-19Bibliographically approved

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CiteExportLink to record
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