Intelligent pancreatitis diagnosis-based on relevance vector machine

Medical diagnostic decision is a fundamental uncertainty event, people always wanted to have an intelligent method approach to this activity. Relevance vector machine is a machine learning method under sparse Bayesian framework, tentatively be applied to help doctors make diagnose diseases decisions. This article for example with the diagnosis of pancreatitis, through the patient's basic information, symptoms with relevance vector machine, determines the severity of patient illness; and compared with the support vector machine and BP neural network. Experiments with relevance vector machine show that the error rate was 22.41%, which is better than support vector machine (24.14%) and BP neural network (25.86%); while the number of relevance vector machine is less than that of support vector. It is illustrated that relevance vector machine is better than both of today's more out-of art methods to diagnose disease in terms of intelligence. It also shows the relevance vector machine has some potential for development in the field of intelligent diagnosis of disease.