Document Type
Article
Publication Date
6-1-2024
Journal / Book Title
Healthcare Analytics
Abstract
The American healthcare system allocates considerable resources compared to peer-developed nations. However, outcomes significantly trail behind, particularly in life expectancy. This study addresses questions about the enduring trends in healthcare spending as a percentage of Gross Domestic Product (GDP), notable factors contributing to this concerning trend, and the timing to apply an emergency brake to curb this accelerating trajectory. Advanced machine learning algorithms, such as Random Forest and Support Vector Regression (SVR), in conjunction with traditional statistical forecasting methods, are used to forecast future patterns. The research underscores the importance of healthcare analytics in unraveling the intricacies of the healthcare system. The findings highlight the pressing need for effective policies to confront this mounting challenge.
DOI
10.1016/j.health.2024.100312
MSU Digital Commons Citation
Wang, John; Qin, Zhaoqiong; Hsu, Jeffrey; and Zhou, Bin, "A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure" (2024). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 162.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/162
Rights
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).