A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion

Mohammad Shahadat Hossain, Emran Hossain, Md. Saifuddin Khalid, Mohammad Ahsanul Haque

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Resumé

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.
OriginalsprogEngelsk
TitelProceedings from Scandinavian Conference on Health Informatics
RedaktørerCarl Erik Moe, Mariann Fossum
Antal sider7
ForlagLinköping University Electronic Press
Publikationsdato2014
Sider83-89
Artikelnummer012
ISBN (Trykt)978-91-7519-241-3
StatusUdgivet - 2014
BegivenhedScandinavian Conference on Health Informatics - University of Agder, Grimstad, Norge
Varighed: 21 aug. 201422 aug. 2014

Konference

KonferenceScandinavian Conference on Health Informatics
LokationUniversity of Agder
LandNorge
ByGrimstad
Periode21/08/201422/08/2014
NavnLinköping Electronic Conference Proceedings
ISSN1650-3686

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Decision support systems
Expert systems
Knowledge representation
Uncertainty

Citer dette

Hossain, M. S., Hossain, E., Khalid, M. S., & Haque, M. A. (2014). A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion. I C. E. Moe, & M. Fossum (red.), Proceedings from Scandinavian Conference on Health Informatics (s. 83-89). [012] Linköping University Electronic Press. Linköping Electronic Conference Proceedings
Hossain, Mohammad Shahadat ; Hossain, Emran ; Khalid, Md. Saifuddin ; Haque, Mohammad Ahsanul. / A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion. Proceedings from Scandinavian Conference on Health Informatics. red. / Carl Erik Moe ; Mariann Fossum. Linköping University Electronic Press, 2014. s. 83-89 (Linköping Electronic Conference Proceedings).
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title = "A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion",
abstract = "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.",
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Hossain, MS, Hossain, E, Khalid, MS & Haque, MA 2014, A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion. i CE Moe & M Fossum (red), Proceedings from Scandinavian Conference on Health Informatics., 012, Linköping University Electronic Press, Linköping Electronic Conference Proceedings, s. 83-89, Grimstad, Norge, 21/08/2014.

A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion. / Hossain, Mohammad Shahadat ; Hossain, Emran; Khalid, Md. Saifuddin; Haque, Mohammad Ahsanul.

Proceedings from Scandinavian Conference on Health Informatics. red. / Carl Erik Moe; Mariann Fossum. Linköping University Electronic Press, 2014. s. 83-89 012 (Linköping Electronic Conference Proceedings).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion

AU - Hossain, Mohammad Shahadat

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AU - Khalid, Md. Saifuddin

AU - Haque, Mohammad Ahsanul

PY - 2014

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N2 - 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.

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Hossain MS, Hossain E, Khalid MS, Haque MA. A Belief Rule-Based (BRB) Decision Support System for Assessing Clinical Asthma Suspicion. I Moe CE, Fossum M, red., Proceedings from Scandinavian Conference on Health Informatics. Linköping University Electronic Press. 2014. s. 83-89. 012. (Linköping Electronic Conference Proceedings).