Conference article

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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Published in: Scandinavian Conference on Health Informatics; August 22; 2014; Grimstad; Norway

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

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Published: 2014-08-20

ISBN: 978-91-7519-241-3

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

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.

Keywords

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

References

[1] Jeffery PK. Remodeling in Asthma and Chronic Obstructive Lung Disease. Am J Respir Crit Care Med 2001;164:S28–S38.

[2] Dold S; Wjst M; von Mutius E; Reitmeir P; Stiepel E. Genetic risk for asthma; allergic rhinitis; and atopic dermatitis. Arch Dis Child 1992;67:1018–22.

[3] Rahman S; Hossain MS. A Belief Rule Based System Prototype for Asthma Suspicion; Khulna; Bangladesh: 2013.

[4] Mathew J; Semenova Y; Farrell G. A miniature optical breathing sensor. Biomed Opt Express 2012;3:3325.

[5] Zolnoori M; Zarandi MHF; Moin M; Teimorian S. Fuzzy Rule-Based Expert System for Assessment Severity of Asthma. J Med Syst 2012;36:1707–17.

[6] Redier H; Daures JP; Michel C; Proudhon H; Vervloet D; Charpin D; et al. Assessment of the severity of asthma by an expert system. Description and evaluation.
Am J Respir Crit Care Med 1995;151:345–52.

[7] Mishra N; Singh D; Bandil MK; Sharma P. Decision Support System for Asthma (DSSA). Int J Inf Comput Technol 2013;3:549–54.

[8] Angulo C; Cabestany J; Rodríguez P; Batlle M; González A; de Campos S. Fuzzy expert system for the detection of episodes of poor water quality through continuous measurement. Expert Syst Appl 2012;39:1011–20.

[9] Liu TI; Singonahalli JH; Iyer NR. Detection of Roller Bearing Defects Using Expert System and Fuzzy Logic. Mech Syst Signal Process 1996;10:595–614.

[10] Russell SJ; Norvig P; Davis E. Artificial intelligence: a modern approach. Upper Saddle River; NJ: Prentice Hall; 2010.

[11] Jian-Bo Yang; Jun Liu; Jin Wang; How-Sing Sii; Hong-Wei Wang. Belief rule-base inference methodology using the evidential reasoning Approach-RIMER. IEEE Trans Syst Man Cybern - Part Syst Hum 2006;36:266–85.

[12] Kong GL; Zu DL; Yang JB. An evidence-adaptive belief rule-based clinical decision support system for clinical risk assessment in emergency care; Bonn; Germany: 2009.

[13] Jian-Bo Yang; Pratyush Sen. A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Trans Syst Man Cybern 1994;24:1458–73.

[14] Wang Y-M; Yang J-B; Xu D-L. Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res 2006;174:1885–913.

[15] Jian-Bo Yang; Jun Liu; Jin Wang; Guo-Ping Liu; Hong-Wei Wang. An optimal learning method for constructing belief rule bases. vol. 1; IEEE; 2004; p. 994–9.

[16] Body R. Clinical decision rules to enable exclusion of acute coronary syndromes in the emergency department. Doctoral Thesis. Manchester Metropolitan University; 2009.

[17] Skalska H; Freylich V. Web-bootstrap estimate of area under ROC curve. Aust J Stat 2006;35:325–30.

[18] Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8:283–98.

[19] Hanley JA. The Robustness of the “Binormal” Assumptions Used in Fitting ROC Curves. Med Decis Making 1988;8:197–203.

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