Extended Belief Rule-Based System With Accurate Rule Weights and Efficient Rule Activation for Diagnosis of Thyroid Nodules

Extended belief-rule-based (EBRB) system is a representative rule-based system and has attracted much attention due to its capability of solving the problems of combinatorial explosion and time-consuming optimization incurred by belief-rule-based system. Despite their advantages, the development of EBRB suffers from some shortcomings, such as the unreasonable calculation of similarity between input and antecedent belief distributions (BDs), inaccurate calculation of individual matching degrees and rule weights, and inefficient determination of activation rules. To address these shortcomings, this article proposes a new EBRB system, in which an existing similarity measure between two BDs is adopted to accurately calculate the individual matching degrees and rule weights. Furthermore, the activation weight of a rule is efficiently calculated within an activation group generated using the affinity propagation algorithm, which is utilized to determine whether the rule is activated. The activation rules are then integrated using their weights and the evidential reasoning algorithm to generate the inference result. To demonstrate its accuracy and efficiency, the proposed EBRB system is employed to help diagnose thyroid nodules based on the historical examination reports collected from a tertiary hospital located in Hefei, Anhui, China. The findings are highlighted by the comparison of the proposed EBRB system with existing EBRB systems based on the historical reports and datasets derived from the University of California at Irvine.