Outbreak Prediction of Hepatitis A in Korea based on Statistical Analysis and LSTM Network

The biggest cause of the epidemic is “unpredictable.” In Korea, the number of patients with hepatitis A has been increasing recently, and the government enacted a law in 1954 to effectively manage diseases such as hepatitis A. The purpose of this paper is to design a model for predicting trends in order to minimize the damage caused by the prevalence of infectious diseases. As a tool for it, various statistical techniques and Supervised Learning of Machine Learning and LSTM techniques were used. To this end, data were collected using Web Crawling and other data, correlation analysis using Regression analysis was conducted, and prediction models were designed using Machine Learning and LSTM.