Machine-learning techniques for feature selection and prediction of mortality in elderly CABG patients

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

Research output: Contribution to journalArticlepeer-review

Abstract

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possi-ble.

Original languageEnglish
Article number547
JournalHealthcare (Switzerland)
Volume9
Issue number5
DOIs
Publication statusPublished - May 2021

Keywords

  • CABG
  • Feature selection
  • Machine learning
  • National health insurance research database
  • NHIRD
  • Older adults
  • Overall survival prediction

ASJC Scopus subject areas

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

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