Journal Article

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction

Jeong, Myung Ho, Lee, Joongyub, Lee, Won Kyung, Woo, Seoung-Il, Bae, Jang-Whan, Choi, Seong Huan, Lee, Woojoo, Jung, Seungpil

2021

Overview

Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 692/308 / 692/308/575 / 692/4019 / Heart attacks / Humanities and Social Sciences / Learning algorithms / Machine learning / Mortality / multidisciplinary / Myocardial infarction / Patients / Prediction models / Regression analysis / Science / Science (multidisciplinary)