H7N9 avian influenza diagnosis based on a multilayer belief rule‐based inference methodology

Abstract H7N9 avian influenza is a novel virus with high morbidity and mortality that threatens human health and life. Therefore, it is necessary to diagnose H7N9 avian influenza in a timely and rapid manner to prevent further transmission of the virus and greatly reduce the infection and mortality rates. This paper proposes an H7N9 avian influenza diagnostic model that is based on a multilayer belief rule‐based (BRB) inference methodology by considering five typical characteristics of influenza: epidemiology, clinical manifestations, complications, characteristics of imaging tests and positive pathogen test results. Specifically, the severity of H7N9 avian influenza is gradually identified by a multilayer BRB model, and then the diagnostic model is optimized by a genetic algorithm (GA) to improve the diagnostic accuracy. Finally, the feasibility of the model is verified by fivefold cross‐validation with a real clinical dataset. The performance of the proposed diagnostic model is compared with those of the BP neural network (BPNN) model and support vector machine (SVM) model, and the results show that the multilayer BRB model can achieve rapid and satisfactory diagnostic results for H7N9 avian influenza. The experiment shows that the accuracy of the BRB model for H7N9 avian influenza hierarchical diagnosis provided in this paper is 0.903, which is higher than 0.818 of the BP neural network (BPNN) modules and 0.844 of the support vector machine (SVM) models. Especially when diagnosing the suspected and confirmed degree of H7N9 disease, it is more realized satisfactory diagnostic accuracy.