Diabetes prediction model based on deep belief network

Diabetes is a disease that seriously endangers human health. Early detection and early treatment can reduce the likelihood of complications and mortality. The predictive model can effectively solve the above problems and provide helpful information for the clinic. Based on this, it is proposed to apply the idea of integrated algorithm in DBN algorithm, collect the hospital data by investigating its related factors, clean and process the collected data, and sample and model the processed data multiple times. It is shown that a single DBN classifier is better than support vector machine and logistic regression algorithm. The model established by the integrated deep confidence network has a significant improvement in classification accuracy compared to a single DBN classifier, and solves the unstable classification effect of a single DBN classifier.