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September 16, 2025International Journal of Electrical and Electronic Engineering & Telecommunications

Optimizing Myocardial Infarction Prediction Performance Using Advanced Machine Learning Techniques

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Authors

SJShridevi K. Jamage

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Overview

This analysis demonstrates improved myocardial infarction prediction accuracy using machine learning models and ECG data.

Key Points

  • The advanced machine learning framework achieved a training accuracy of 1.00, highlighting its effectiveness.
  • The deep learning model classifies heartbeat types with a testing accuracy of 0.98, ensuring reliable diagnosis.
  • Integrating public and clinical datasets enhances the robustness and generalizability of the predictions.
  • The study's innovative approach may significantly improve clinical deployment and patient outcomes.

Cite This Study

Shridevi K. Jamage (2025) studied this question.

synapsesocial.com/papers/68d42328713b0b5dfea6b06bhttps://doi.org/10.18178/ijeetc.14.5.282-295
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Also Consider

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

  1. 1Machine Learning for Mortality Risk Prediction in Myocardial Infarction: A Clinical-Economic Decision Support Framework2025
  2. 2Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods2026
  3. 3Integrating Manual ECG Feature Extraction with Ensemble Learning for Myocardial Infarction Diagnosis2025
  4. 4Cardiovascular Disease Prediction Using Machine Learning2025 · 1 citations
  5. 5Machine Learning-Based Risk Stratification for Sudden Cardiac Death Using Clinical and Device-Derived Data2025