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

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Artificial Intelligence in Medicine (AIME 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11526))

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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.

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Notes

  1. 1.

    https://www.nhsgoldenjubilee.co.uk/.

  2. 2.

    Attributes are: Age, sex, diabetes, body mass index, smoking status, surgical priority, critical preoperative state, procedure, left main stem, extracardiac arteriopathy, pulmonary disease, creatinine level, renal impairment, New York Heart Association grade, angina status, rhythm, left ventricular function, neurological dysfunction, congestive cardiac failure, previous myocardial infarction, active endocarditis, hypertension, previous cardiac surgery, previous percutaneous coronary intervention.

  3. 3.

    Severe complications in this study include: Acute renal failure, deep sternal wound infection, septicemia, transient stroke, tracheostomy, cardiac arrest, permanent stroke, severe heart failure, adult respiratory distress syndrome, multi-organ failure, mesenteric infarction, required laparotomy, severe pulmonary edema, left ventricular wall dissection, hepatic failure, reopening requiring coronary artery bypass graft, paraparesis, and amputation.

  4. 4.

    http://apps.who.int/classifications/icd10/browse/2010/en.

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Authors and Affiliations

  1. University of Strathclyde, Glasgow, G1 1XH, UK

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

  2. Golden Jubilee National Hospital, Clydebank, G81 4DY, UK

    Stefan Schraag

Authors
  1. Linda Lapp
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  2. Matt-Mouley Bouamrane
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  3. Kimberley Kavanagh
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  4. Marc Roper
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  5. David Young
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  6. Stefan Schraag
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Correspondence to Linda Lapp .

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Editors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

  2. Poznan University of Technology, Poznan, Poland

    Szymon Wilk

  3. VU Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

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Lapp, L., Bouamrane, MM., Kavanagh, K., Roper, M., Young, D., Schraag, S. (2019). Evaluation of Random Forest and Ensemble Methods at Predicting Complications Following Cardiac Surgery. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_48

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  • DOI: https://doi.org/10.1007/978-3-030-21642-9_48

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  • Online ISBN: 978-3-030-21642-9

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