![]() ![]() A prediction of the severity and prognosis is vital for identifying patients at high risk and providing intensive treatment and monitoring 3. 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.Īcute myocardial infarction (AMI) is a leading cause of mortality despite recent advances in percutaneous coronary intervention (PCI) based on the use of drug-eluting stents and pharmacotherapy, including beta-blockers and the renin-angiotensin system blocker 1, 2. 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. 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. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. 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. ![]() 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). Machine learning (ML) has been suggested to improve the performance of prediction models. ![]()
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