A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure

John Wang, Zhaoqiong Qin, Jeffrey Hsu, Bin Zhou

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number100312
JournalHealthcare Analytics
Volume5
DOIs
StatePublished - Jun 2024

Keywords

  • AutoRegressive integrated moving average
  • Healthcare analytics
  • Healthcare expenditure
  • Random forest
  • Support vector machine
  • Support vector regression

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