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October 3, 2025MedicinaOpen Access

Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry

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Authors

JJJi-Hoon JungKLKyusup LeeKCKiyuk Chang

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Overview

Analysis of various machine learning techniques shows random forest excelled in predicting major adverse cardiac events in AMI patients, highlighting clinical implications.

Key Points

  • The random forest model achieved the highest predictive performance for major adverse cardiac events in patients with acute myocardial infarction.
  • At 5 years, the random forest model had an area under the curve of 0.822 and an accuracy of 0.804, outperforming other models tested.
  • Analysis of predictors for adverse outcomes identified key factors including age, renal function, and adherence to optimal medical therapy.
  • This study emphasizes the ongoing role of guideline-directed medical therapy in improving prognosis for patients with acute myocardial infarction.

Cite This Study

Jung et al. (2025) studied this question.

synapsesocial.com/papers/68e034f7f0e39f13e7fa3226https://doi.org/10.3390/medicina61101783
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis2025 · 10 citations
  2. 2Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification2025
  3. 3Predicting 3-year mortality after myocardial infarction in a structured care program: A comparative analysis of Cox regression and machine learning models2026
  4. 4Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning–Based Time-to-Event Analysis2025
  5. 5Developing an ICU Mortality Risk Prediction Model for Acute Myocardial Infarction Patients Based on Machine Learning2025