Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms

Reza Ghafari; Amir Sorayaie Azar; Ali Ghafari; Fatemeh Moradabadi Aghdam; Morteza Valizadeh; Naser Khalili; Shima Hatamkhani

doi:10.18502/jthc.v18i4.14827

Reza Ghafari Pharmacy Faculty, Urmia University of Medical Sciences, Urmia, Iran.
Amir Sorayaie Azar Department of Computer Engineering, Urmia University, Urmia, Iran.
Ali Ghafari Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Fatemeh Moradabadi Aghdam Pharmacy Faculty, Urmia University of Medical Sciences, Urmia, Iran.
Morteza Valizadeh Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.
Naser Khalili Department of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Shima Hatamkhani Experimental and Applied Pharmaceutical Sciences Research Center, Urmia University of Medical Sciences, Urmia, Iran.

DOI: https://doi.org/10.18502/jthc.v18i4.14827

Keywords: Artificial intelligence; Machine learning; Myocardial infarction; Prognosis; Mortality

Abstract

Background: Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission.

Methods: We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC).

Results: In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm.

Conclusion: XGBoost algorithms can be a promising model for predicting fatal complications following MI.