Primary percutaneous coronary intervention (PPCI) is a minimally invasive procedure to unblock the arteries which carry blood to the heart. Referred patients are accepted or turned down for PPCI mainly based on the presence of ST segment elevation on the surface electrocardiogram. We explored the features which predict 30 days and 1-year mortality in accepted and turndown patients and report the performance of machine learning (ML) algorithms. Different ML algorithms, namely multiple logistic regression (MLR), decision tree (DT), and a support vector machine (SVM) were used for the prediction of 30 days and 1-year mortality. Upon significance of various features to predict the 30 days and 1-year mortality, the accuracy, sensitivity, and specificity were compared between algorithms. DT outperformed the other algorithms (SVM and MLR) to predict mortality of patients referred to the PPCI service. Greater sensitivity is achieved in predicting 30 days mortality in the accepted group compared to the turndown group, however, the former model included more features.
|Title of host publication||2020 Computing in Cardiology, CinC 2020|
|Place of Publication||Rimini, Italy|
|Publication status||Published - 10 Feb 2021|
|Event||Computing in Cardiology 2020 - Palacongressi, Rimini, Italy|
Duration: 13 Sep 2020 → 16 Sep 2020
|Name||Computing in Cardiology|
|Conference||Computing in Cardiology 2020|
|Period||13/09/20 → 16/09/20|
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research is supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB).
Copyright 2021 Elsevier B.V., All rights reserved.
- Machine Learning
- PPCI Referral