Disease Prediction Model based on Neural Network ARIMA Algorithm

Abstract

Because the morbidity data of infectious diseases do not only have a single linear or nonlinear characteristic, but also have both linear and nonlinear characteristics, the combination model prediction method is often used to predict the morbidity of infectious diseases in recent years. Compared with the single model prediction analysis method, the combination model can combine the advantages of a single model to extract the effective information contained in the original time series more scientifically and fully. In the context of big data, for the medical field, massive medical data is complex, and the traditional manual data processing method has been unable to meet the current needs. With the help of the computer, data mining can discover new knowledge that is potentially useful and understandable by clearing, integrating, selecting, and transforming the original data. Using data mining, we can organize and reproduce the useful medical knowledge hidden in medical big data. In this paper, an ARIMA-GRNN model is established; the fitting value and the corresponding time are used as the input of the neural network. The actual morbidity is used as the output to train the network and construct the ARIMA-GRNN combined model. Due to the different information flow of BP neural network and neural network, this study also constructed ARIMA-GRNN combined model and ARIMA model, and compared the modeling effect and prediction performance of various models. The average absolute percentage error of the experimental results in this paper is less than 8.63%, and the average absolute percentage error is less than 5%. Compared with other models, it has a better prediction effect, higher accuracy, and more obvious advantages. In this paper, the prediction of disease is dynamic and continuous. It is of great significance for disease prevention and control to use monitoring data to study the epidemic trend and periodic change law, and to make a reasonable prediction.