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The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts’ suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES’s generated results are more reliable than that of human expert as well as fuzzy rule based expert system.
This article described the design, implementation and applications of a Belief Rule Based Expert system (BRBES), allowing the measurement of the level of suspicion of TB by taking account of its various signs and symptoms. The system allows the understanding of the relationship between signs and symptoms and the level of suspicion of TB of a patient in an explicit and transparent way. This will allow the identification of the signs and symptoms those are responsible for increasing the suspicion level of TB of a patient. In this way, various scenarios by taking account of the signs and symptoms of a patient from various perspectives can be carried out by using the BRBES. The physicians will eventually be able to select the appropriate medicine for a patient. In addition, all types of uncertainties such as vagueness, imprecision, ambiguity, ignorance and incompleteness can be handled in an integrated framework which makes the system more robust as evident from the comparative results generated by using both the manual system and the fuzzy rule based expert system as illustrated in Table 6. Therefore, the BRBES can provide a decision support platform to the physicians and would serve a savior by offering primary health care to the people with reduced time and low diagnosis cost. In future, the BEBES would be extended to support optimal learning by training the knowledge representation parameters such as rule weight, attribute weight, and belief degrees.