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

Lapp, Linda and Bouamrane, Matt-Mouley and Kavanagh, Kimberley and Roper, Marc and Young, David and Schraag, Stefan; Wilk, Szymon and ten Teije, Annette and Riaño, David, eds. (2019) Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery. In: Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, Cham, 376–385. ISBN 9783030216429 (https://doi.org/10.1007/978-3-030-21642-9_48)

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Abstract

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.

ORCID iDs

Bouamrane, Matt-Mouley ORCID: https://orcid.org/0000-0002-1416-751X

Kavanagh, Kimberley ORCID: https://orcid.org/0000-0002-2679-5409

Roper, Marc ORCID: https://orcid.org/0000-0001-6794-4637

Young, David ORCID: https://orcid.org/0000-0002-3652-0513

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• Item metadata

  Item type:	Book Section
  ID code:	68672
  Dates:	Date Event 30 May 2019 Published 5 April 2019 Accepted
  Notes:	This is a post-peer-review, pre-copyedit version of an article published in Artificial Intelligence in Medicine. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-21642-9_48.
  Subjects:	Science > Mathematics > Electronic computers. Computer science
  Department:	Faculty of Science > Computer and Information Sciences Strategic Research Themes > Health and Wellbeing Faculty of Science > Mathematics and Statistics Strategic Research Themes > Measurement Science and Enabling Technologies
  Depositing user:	Pure Administrator
  Date deposited:	02 Jul 2019 11:13
  Last modified:	11 Nov 2024 15:18
  URI:	https://strathprints.strath.ac.uk/id/eprint/68672