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A Belief Rule Based Expert System to Diagnose Measles under Uncertainty
University of Chittagong.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Department of Computer Science and Engineering, University of Chittagong.
2015 (English)In: Proceedings of the 2015 International Conference on Health Informatics and Medical Systems (HIMS'15) / [ed] Hamid R. Arabnia; Leonidas Deligiannidis; George Jandieri; Ashu M. G. Solo; Fernando G. Tinetti, CSREA Press, 2015, 17-23 p.Conference paper (Refereed)
Abstract [en]

Measles is a highly infectious child disease that causes serious complications and death worldwide. Measles is generally diagnosed from its signs and symptoms by a physician, which cannot be measured with 100% certainty during the diagnosis process. Consequently, the traditional way of diagnosing measles from its signs and symptoms lacks the accuracy. Therefore, a belief rule-based inference methodology using evidential reasoning approach (RIMER), which is capable of handling various types of uncertainties has been used to develop an expert system to diagnose measles under uncertainty. The results, generated, from the system have been compared with the expert opinion as well as with a Fuzzy Logic based system. In both the cases, it has been found that the Belief Rule Based Expert (BRBES), presented in this paper, is more reliable and accurate.

Place, publisher, year, edition, pages
CSREA Press, 2015. 17-23 p.
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
URN: urn:nbn:se:ltu:diva-38066Local ID: c5537d93-e3e2-4e74-9bb7-0efef8058f85ISBN: 1-60132-416-2OAI: diva2:1011565
World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP'15) : The 2015 International Conference on Health Informatics and Medical Systems 27/07/2015 - 30/07/2015
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Godkänd; 2015; 20150525 (karand)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

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