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.
|Konference||Scandinavian Conference on Health Informatics|
|Lokation||University of Agder|
|Periode||21/08/2014 → 22/08/2014|
|Navn||Linköping Electronic Conference Proceedings|