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August 21, 2025Applied SciencesOpen Access

Machine Learning for Mortality Risk Prediction in Myocardial Infarction: A Clinical-Economic Decision Support Framework

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Authors

KFKonstantinos Panagiotis FourkiotisATAthanasios Tsadiras

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Overview

Machine learning improves mortality risk prediction in myocardial infarction, indicating enhanced clinical decision-making potential.

Key Points

  • SVM achieved the highest F1 score of 0.6905 and ROC-AUC of 0.8970, demonstrating effective mortality prediction.
  • The study used a dataset of 1547 patients with multiple features, employing machine learning models for improved outcomes.
  • Feature selection utilized Random Forest, while SMOTE addressed class imbalance for training model effectiveness.
  • The framework shows potential for clinical decision-making, supporting better resource allocation in post-MI care.

Cite This Study

Fourkiotis et al. (2025) studied this question.

synapsesocial.com/papers/68af6d967567bf4f94feb4f9https://doi.org/10.3390/app15169192
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Also Consider

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

  1. 1Optimizing Myocardial Infarction Prediction Performance Using Advanced Machine Learning Techniques2025
  2. 2Developing an ICU Mortality Risk Prediction Model for Acute Myocardial Infarction Patients Based on Machine Learning2025
  3. 3Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification2025
  4. 4Predicting 3-year mortality after myocardial infarction in a structured care program: A comparative analysis of Cox regression and machine learning models2026
  5. 5Explainable Artificial Intelligence-Driven Risk Assessment for Malignant Ventricular Arrhythmia and Mortality in Acute Myocardial Infarction2025