A Belief Rule Based (BRB) Decision Support System to Assess Clinical Asthma Suspicion

Mohammad Shahadat Hossain
Department of Computer Science and Engineering, University of Chittagong, Bangladesh

Emran Hossain
Department of Computer Science and Engineering, University of Chittagong, Bangladesh

Saifuddin Khalid
Department of Learning and Philosophy, Aalborg University, Denmark

Mohammad A. Haque
Department of Architecture, Design and Media Technology, Aalborg University, Denmark

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Ingår i: Scandinavian Conference on Health Informatics; August 22; 2014; Grimstad; Norway

Linköping Electronic Conference Proceedings 102:12, s. 83-89

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Publicerad: 2014-08-20

ISBN: 978-91-7519-241-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Asthma is a common chronic disease that affects millions of people around the world. The most common signs and symptoms of asthma are cough; breathlessness; wheeze; chest tightness and respiratory rate. They cannot be measured accurately since they consist of various types of uncertainty such as vagueness; imprecision; randomness; ignorance and incompleteness. Consequently; traditional disease diagnosis; which is performed by a physician; cannot deliver accurate results. Therefore; this paper presents the design; development and application of a decision support system for assessing asthma under conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system; which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than the results generated by a manual system.


Belief Rule Base; Uncertainty; RIMER; Asthma; Suspicion; Decision Support System; Inference


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