Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery

Mouley Bouamrane

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

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.
Original languageEnglish
Title of host publication17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings
EditorsDavid Riaño, Szymon Wilk, Annette ten Teije
PublisherSpringer
Pages376-385
Volume 11526
EditionLNCS / LNAI
ISBN (Electronic)978-3-030-21642-9
ISBN (Print)978-3-030-21641-2
Publication statusPublished - 26 Jun 2019

Keywords / Materials (for Non-textual outputs)

  • Perioperative Medicine
  • Machine Learning
  • Predictive Modelling
  • Risk Assessment

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