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.

Facial Expression Recognition using Convolutional Neural Network with Data Augmentation

“…However, we are planning to work with compound emotion classes such as surprised with happiness, surprised with anger, sadness with anger, surprised with sadness and so on. In addition, an aggregated view of the facial expression by combining different emotions as well as compound emotions will be determined under uncertainty by using sophisticated methodology like Belief Rule Based Expert Systems (BRBES) in an integrated framework [22] [23] [24] [25] [26]. As different problems would require different network architectures it is required to figure out which architecture is the best for a particular problem.…”

A Belief Rule Based Expert System for Evaluating Technological Innovation Capability of High-Tech Firms Under Uncertainty

“…Each antecedent attribute is linked with referential values while belief degrees are embedded with consequent attributes. BRB contains various learning or knowledge representation parameters such as attribute weight, rule weight, and belief degrees which are used to capture uncertainty in data [11], [12], [13]. A belief rule is presented below: IF Fund Raising Ability is High AND Optimal Capital Allocation is High AND Intensity of Capital Input is Medium AND Return on Investment is Low THEN Capital Capabilities is (High, 0.3), (Medium, 0.7), (Low, 0.0) In this rule, "Fund Raising Ability", "Optimal Capital Allocation", "Intensity of Capital Input" and "Return on Investment" are the antecedent attributes.…”

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

“…The rule-based expert systems uses rules to represent the expert knowledge and these rules are always called upon whenever they are needed to resolve issues, though it has challenges such as ineffective search system, imprecision acceptance, flexibility, knowledge breakthrough a well as its lack of capacity to learn [13]. Longestablished rule-based expert systems are being utilized globally to diagnose medical conditions such as malaria and typhoid [45], fever [46], viral infection [47], influenza [48], memory loss disease [49], Lassa fever [50], [51], dengue fever [52], blood testing [53], and human diseases [54]- [55]. ES will continue to advance for unambiguous utilizations in medical diagnosis owing to invasion of novel and enormous information, which makes experts to be dedicated [56].…”