Machine Learning to Predict 30 Days and 1-Year Mortality in STEMI and Turndown Patients

Aleeha Iftikhar, RR Bond, Khaled Rjoob, Charles Knoery, Stephen James Leslie, Anne McShane, V. E. McGilligan, Aaron Peace

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2020 Computing in Cardiology, CinC 2020
Place of PublicationRimini, Italy
ISBN (Electronic)978-1-7281-7382-5
DOIs
Publication statusPublished - 10 Feb 2021
EventComputing in Cardiology 2020 - Palacongressi, Rimini, Italy
Duration: 13 Sep 202016 Sep 2020

Publication series

NameComputing in Cardiology
Volume2020-September
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X

Conference

ConferenceComputing in Cardiology 2020
Abbreviated titleCinC20
CountryItaly
CityRimini
Period13/09/2016/09/20

Keywords

  • Machine Learning
  • PPCI Referral
  • cathlab
  • cardiology
  • angioplasty

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