Statistics and forecasting analysis on hospital garbage data

For a long time, China's medical problem is very serious. There are very few researches on medical waste data, which can not provide enough evidence for managers. Therefore, combining some methods of data analysis and data mining to analyze the medical waste data.In this study, based on the collection of the hospital garbage data of over five years from some area in China, the hospital garbage data are analyzed with consideration of the location, the hospital level, hospital beds and number of doctors and staff members, by using some data analysis and data mining methods. The time series analysis of garbage data proves that the medical wastes so produced are on the rise and the sharing of the burden of medical missions is unbalanced with regard to the hospital location and levels. By establishing an auto regressive integrated moving average（ARIMA）(0,1,1) model, the prediction and analysis for the every-day production of the hospital wastes in the area are made.The research results of the K-Means clustering analysis and the PARETO contribution analysis provide some empirical evidences for the future planning and development of the hospitals in this area. © 2020, North Atlantic University Union NAUN. All rights reserved.