A belief rule based (BRB) system to assess asthma suspicion

2014 9th International Forum on Strategic Technology (IFOST)

Khalid

Akter

et al. 2014

Self Cite

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.

Section: Overview Of Rimer Methodologymentioning

confidence: 99%

Section: B Inference System With Brbmentioning

confidence: 99%

Section: B Inference System With Brbmentioning

confidence: 99%

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A belief rule-based expert system to diagnose influenza

2014 9th International Forum on Strategic Technology (IFOST)

Khalid

Akter

et al. 2014

Self Cite

“…Unlike ML and DL algorithms, BRBES can address nonlinear causal data, reason under uncertainity and can be optimized using gradient-free nature-inspired algorithms such as BRBaDE [18]. BRBES has been applied to design artificial intelligence-based diagnosis systems for various diseases and medical conditions [32][33][34][35][36][37][38][39]. In [40], BRBES optimized with a modified differential evolution (DE) algorithm has been developed to predict the severity of illness in COVID-19 patients.…”

Section: Literature Reviewmentioning

confidence: 99%

A BRBES to Support Diagnosis of COVID-19 Using Clinical and CT Scan Data

Raihan

Islam

Lecture Notes on Data Engineering and Communications Technologies

et al. 2021

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In the prevailing COVID-19 pandemic, accurate diagnosis plays a vital role in preventing the mass transmission of the SARS-CoV-2 virus. Especially patients with pneumonia need correct diagnosis for proper treatment of their respiratory distress. However, the current standard diagnosis method, RT-PCR testing has a significant false negative and false positive rate. As alternatives, diagnosis methods based on artificial intelligence can be applied for faster and more accurate diagnosis. Currently, various machine learning and deep learning techniques are being researched on to develop better COVID-19 diagnosis system. However, these approaches do not consider the uncertainty in data. Deep learning approaches use backpropagation. It is an unexplainable black box approach and is prone to problems like catastrophic forgetting. This article applies a belief rule-based expert system (BRBES) for diagnosis of COVID-19 on hematological data and CT scan data of lung tissue infection of adult pneumonia patients. The system is optimized with nature-inspired optimization algorithm-BRBES-based adaptive differential evolution (BRBaDE). This model has been evaluated on a real-world dataset of COVID-19 patients published in a previous work. Also, performance of the BRBaDE has been compared with BRBES optimized with genetic algorithm and MATLAB's fmincon function where BRBaDE outperformed genetic algorithm and fmincon and showed best accuracy of 73.91%.

“…However, in most situations where places are identified as at risk of flooding, there is little data available with which to estimate either the probability of exceedence of a given discharge or to predict the depths and velocities resulting from such a discharge. Flood frequency estimates, whether estimated by fitting statistical distributions, by regionalization from gauged sites, or by continuous simulation [23][24] are known to be highly uncertain. The predictions of flood inundation models, even when conditioned on observed depth or flooded area information, are also known to be uncertain (and in some cases cannot match the observations throughout the flow domain) [25].…”

Section: Smart Risk Assessment Systemsmentioning

confidence: 99%

Smart risk assessment systems using belief-rule-based DSS and WSN technologies

Andersson

2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace &Amp; Elec

2014

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Abstract-Smart risk assessment systems are becoming more and more important in the society. If the chances of reducing and managing certain [[risks are increased, the impacts can be controlled and reduced significantly. This article surveys different belief-rule-based decision support systems and various wireless sensor network technologies that can be used in collaboration to build interesting risk assessment applications. We propose a model for building such an environment and describe a potential application of our proposed model for assessing flood risks in a case study.