데이터 예측을 위한 통계적 방법 비교 및 활용

Abstract

Healthcare data forecasting is used in a variety of fields, including policy evaluation, health insurance financial estimation, and the detection of unusual claims symptoms. Furthermore, the evidence generated by the predictive model is critical in the development of health policies and decision-making processes. As a result, the prediction process must be scientific and systematic, and accuracy measures should be a key selection criterion for forecasting methods. By examining the data prediction methods used in the health care field as well as the most recent forecasting techniques, we attempted to suggest a forecasting method suitable for the characteristics of healthcare data in this study. To that end, a literature review, investigations on the most recent forecasting methodologies, and a comparative analysis of forecasting performance based on data type were performed. And the results of the analysis were synthesized to suggest a forecasting method appropriate for the type of healthcare data. The subject of forecasting method review included regression models, time series models, and machine learning. The data types were separated into continuous variables and count variables, and data sets of 12, 24, and 30 sizes were created. Health insurance claim data was used to compare forecasting performance, and SAS and Python were used as analysis tools. According to the findings, the machine learning forecasting method performed best for both continuous and count data types, while the ARIMA, time series analysis method performed reasonably well for continuous variables. Forecasting performance improved as the number of data points increased. In this study, we recommend a sample size of at least 30 subjects. This research is anticipated to help in the selection of an appropriate forecasting method for performing complex prediction tasks.