Chicken pox prediction using deep learning model

Chicken pox is a highly diffuse disease, and the need for surveillance research to predict it is increasing. Initially used CDC data takes at least a week to a month for this data to be confirmed. So there is a need to predict chicken pox using web data that can be collected in real time. Chicken pox, unlike other infectious diseases, appears frequently in web data regardless of actual outbreak data. Therefore, their linear relationship is not clear enough to be applied to existing linear regression models. In this paper, we predict chicken pox through deep learning model that can model nonlinear relationship. In addition, the prediction accuracy is improved by extracting the keyword related to the outbreak of chicken pox. Finally, the LSTM prediction model was able to predict the chicken pox for a longer period of time and had the highest correlation coefficient of 0.97114. The root mean square error was 341.01547, which was overwhelmingly smaller than the linear regression model.