Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Linda Lapp, Matt-Mouley Bouamrane, Kimberley Kavanagh, Marc Roper, David Young, Stefan Schraag

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

8 Citations (Scopus)
25 Downloads (Pure)


Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditorsSzymon Wilk, Annette ten Teije, David Riaño
Place of PublicationCham
Number of pages10
ISBN (Electronic)9783030216429
ISBN (Print)9783030216412
Publication statusPublished - 30 May 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • postoperative complications
  • machine learning
  • cardiac surgery


Dive into the research topics of 'Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery'. Together they form a unique fingerprint.

Cite this