Abstract
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.
Highlights
Machine learning (ML) has been suggested to improve the performance of prediction models
Traditional risk stratification was based on risk score systems, such as the thrombolysis in myocardial infarction (TIMI), global registry of acute coronary events (GRACE), and acute coronary treatment and intervention outcomes network—Get With The Guidelines (ACTION-GWTG), which extracts the weight from the regression m odel[3,4,5,6,7,8,9,10]
GRACE and ACTION-GWTG presented a common model for ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), whereas TIMI suggested two distinct risk stratifications
Summary
Machine learning (ML) has been suggested to improve the performance of prediction models. 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. GRACE and ACTION-GWTG presented a common model for ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), whereas TIMI suggested two distinct risk stratifications These models were validated and are commonly accepted tools, concerns have been raised recently because most traditional risk stratifications were developed 20 years ago using randomized controlled trial (RCT) data before the introduction of drug-eluting stents and newer generation antiplatelets[11]. This study compared the performance of ML models in predicting the short- and long-term mortality using comparable predictors in AMI patients with the traditional risk score methods. This study examined whether adding more predictors to the ML models would improve the performance of the prediction models
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