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Machine Learning to Predict 30 days and 1-year Mortality in STEMI and Turndown Patients
- Source :
- CinC
- Publication Year :
- 2020
- Publisher :
- Computing in Cardiology, 2020.
-
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.
- Subjects :
- business.industry
medicine.medical_treatment
Decision tree
Percutaneous coronary intervention
030204 cardiovascular system & hematology
Machine learning
computer.software_genre
Logistic regression
Support vector machine
03 medical and health sciences
0302 clinical medicine
Angioplasty
ST segment
Medicine
030212 general & internal medicine
Artificial intelligence
business
1 year mortality
computer
Minimally invasive procedures
Subjects
Details
- ISSN :
- 2325887X
- Database :
- OpenAIRE
- Journal :
- 2020 Computing in Cardiology Conference (CinC)
- Accession number :
- edsair.doi...........7852dc567a65b3c1ccd3d41c90ad1047