A Belief Rule-Based Expert System to Diagnose Influenza

Mohammad Shahadat Hossain, Md. Saifuddin Khalid, Shamima Akter, Shati Dey

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Resumé

Influenza is a viral disease that usually affects the nose, throat, bronchi, and seldom lungs. This disease spreads as seasonal epidemics around the world, with an annual attack rate of estimated at 5%–10% in adults and 20%–30% in children. Thus, influenza is regarded as one of the critical health hazards of the world. Early diagnosis (consisting of determination of signs and symptoms) of this disease can lessen its severity significantly. Examples of signs and symptoms of this disease consist of cough, fever, headache, bireme, nasal congestion, nasal polyps and sinusitis. These signs and symptoms cannot be measured with near-100% certainty due to varying degrees of uncertainties such as vagueness, imprecision, randomness, ignorance, and incompleteness. Consequently, traditional diagnosis, carried out by a physician, is unable to deliver desired accuracy. Hence, this paper presents the design, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy.
OriginalsprogEngelsk
TitelProceedings of the 9th International Forum on Strategic Technology
ForlagIEEE Professional Communication Society
Publikationsdato2014
Sider113 - 116
ISBN (Trykt)978-1-4799-6060-6
DOI
StatusUdgivet - 2014
BegivenhedInternational Forum on Strategic Technology (IFOST) 2014 - Long Beach Hotel, Cox's Bazar, Bangladesh
Varighed: 21 okt. 201423 nov. 2014

Konference

KonferenceInternational Forum on Strategic Technology (IFOST) 2014
LokationLong Beach Hotel
LandBangladesh
ByCox's Bazar
Periode21/10/201423/11/2014

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Expert systems
Health hazards
Knowledge representation
Uncertainty

Citer dette

Hossain, M. S., Khalid, M. S., Akter, S., & Dey, S. (2014). A Belief Rule-Based Expert System to Diagnose Influenza. I Proceedings of the 9th International Forum on Strategic Technology (s. 113 - 116). IEEE Professional Communication Society. https://doi.org/10.1109/IFOST.2014.6991084
Hossain, Mohammad Shahadat ; Khalid, Md. Saifuddin ; Akter, Shamima ; Dey, Shati. / A Belief Rule-Based Expert System to Diagnose Influenza. Proceedings of the 9th International Forum on Strategic Technology. IEEE Professional Communication Society, 2014. s. 113 - 116
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title = "A Belief Rule-Based Expert System to Diagnose Influenza",
abstract = "Influenza is a viral disease that usually affects the nose, throat, bronchi, and seldom lungs. This disease spreads as seasonal epidemics around the world, with an annual attack rate of estimated at 5{\%}–10{\%} in adults and 20{\%}–30{\%} in children. Thus, influenza is regarded as one of the critical health hazards of the world. Early diagnosis (consisting of determination of signs and symptoms) of this disease can lessen its severity significantly. Examples of signs and symptoms of this disease consist of cough, fever, headache, bireme, nasal congestion, nasal polyps and sinusitis. These signs and symptoms cannot be measured with near-100{\%} certainty due to varying degrees of uncertainties such as vagueness, imprecision, randomness, ignorance, and incompleteness. Consequently, traditional diagnosis, carried out by a physician, is unable to deliver desired accuracy. Hence, this paper presents the design, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy.",
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Hossain, MS, Khalid, MS, Akter, S & Dey, S 2014, A Belief Rule-Based Expert System to Diagnose Influenza. i Proceedings of the 9th International Forum on Strategic Technology. IEEE Professional Communication Society, s. 113 - 116, Cox's Bazar, Bangladesh, 21/10/2014. https://doi.org/10.1109/IFOST.2014.6991084

A Belief Rule-Based Expert System to Diagnose Influenza. / Hossain, Mohammad Shahadat ; Khalid, Md. Saifuddin; Akter, Shamima; Dey, Shati.

Proceedings of the 9th International Forum on Strategic Technology. IEEE Professional Communication Society, 2014. s. 113 - 116.

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

TY - GEN

T1 - A Belief Rule-Based Expert System to Diagnose Influenza

AU - Hossain, Mohammad Shahadat

AU - Khalid, Md. Saifuddin

AU - Akter, Shamima

AU - Dey, Shati

PY - 2014

Y1 - 2014

N2 - Influenza is a viral disease that usually affects the nose, throat, bronchi, and seldom lungs. This disease spreads as seasonal epidemics around the world, with an annual attack rate of estimated at 5%–10% in adults and 20%–30% in children. Thus, influenza is regarded as one of the critical health hazards of the world. Early diagnosis (consisting of determination of signs and symptoms) of this disease can lessen its severity significantly. Examples of signs and symptoms of this disease consist of cough, fever, headache, bireme, nasal congestion, nasal polyps and sinusitis. These signs and symptoms cannot be measured with near-100% certainty due to varying degrees of uncertainties such as vagueness, imprecision, randomness, ignorance, and incompleteness. Consequently, traditional diagnosis, carried out by a physician, is unable to deliver desired accuracy. Hence, this paper presents the design, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy.

AB - Influenza is a viral disease that usually affects the nose, throat, bronchi, and seldom lungs. This disease spreads as seasonal epidemics around the world, with an annual attack rate of estimated at 5%–10% in adults and 20%–30% in children. Thus, influenza is regarded as one of the critical health hazards of the world. Early diagnosis (consisting of determination of signs and symptoms) of this disease can lessen its severity significantly. Examples of signs and symptoms of this disease consist of cough, fever, headache, bireme, nasal congestion, nasal polyps and sinusitis. These signs and symptoms cannot be measured with near-100% certainty due to varying degrees of uncertainties such as vagueness, imprecision, randomness, ignorance, and incompleteness. Consequently, traditional diagnosis, carried out by a physician, is unable to deliver desired accuracy. Hence, this paper presents the design, development and application of an expert system to diagnose influenza under uncertainty. The recently developed generic belief rule-based inference methodology by using the evidential reasoning (RIMER) approach is employed to develop this expert system, termed as Belief Rule Based Expert System (BRBES). The RIMER approach can handle different types of uncertainties, both in knowledge representation, and in inference procedures. The knowledge-base of this system was constructed by using records of the real patient data along with in consultation with the Influenza specialists of Bangladesh. Practical case studies were used to validate the BRBES. The system generated results are effective and reliable than from manual system in terms of accuracy.

U2 - 10.1109/IFOST.2014.6991084

DO - 10.1109/IFOST.2014.6991084

M3 - Article in proceeding

SN - 978-1-4799-6060-6

SP - 113

EP - 116

BT - Proceedings of the 9th International Forum on Strategic Technology

PB - IEEE Professional Communication Society

ER -

Hossain MS, Khalid MS, Akter S, Dey S. A Belief Rule-Based Expert System to Diagnose Influenza. I Proceedings of the 9th International Forum on Strategic Technology. IEEE Professional Communication Society. 2014. s. 113 - 116 https://doi.org/10.1109/IFOST.2014.6991084