Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

O. Parise*, G. Parise, A. Vaidyanathan, M. Occhipinti, A. Gharaviri, C. Tetta, E. Bidar, B. Maesen, J.G. Maessen, M. La Meir, S. Gelsomino

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors. Methods: Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models. Results: Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score. Conclusions: The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.
Original languageEnglish
Article number82
Number of pages15
JournalJournal of Cardiovascular Development and Disease
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Feb 2023

Keywords

  • machine learning
  • artificial intelligence
  • atrial fibrillation
  • postoperative CABG
  • PULMONARY VEIN ISOLATION
  • CARDIAC-SURGERY
  • PREDICTION MODELS
  • INVASIVE SURGERY
  • COMPUTER-MODEL
  • PREVENTION
  • STROKE
  • EPIDEMIOLOGY
  • MORTALITY
  • DISEASE

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