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.