Abstract

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.