The prediction model of medical expenditure appling machine learning algorithm in cabg patients

Yen Chun Huang, Shao Jung Li, Mingchih Chen, Tian Shyug Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

Original languageEnglish
Article number710
JournalHealthcare (Switzerland)
Volume9
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • CABG
  • Feature selection
  • Machine learning
  • Medical expenditure predict
  • National health insurance research database
  • NHIRD

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy
  • Health Information Management
  • Leadership and Management

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